CN111090983B - Questionnaire optimization method, device, computer equipment and storage medium - Google Patents
Questionnaire optimization method, device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a questionnaire optimization method, a device, a computer device and a storage medium, comprising: obtaining a questionnaire answer data set and a corresponding questionnaire question set, inputting the questionnaire answer data set into an association rule calculation model, calculating the association degree between each questionnaire answer data in the questionnaire answer data set through the association rule calculation model to obtain an answer association table, constructing a question association table according to the association answer contained in the answer association table, the associated answer, the answer support degree, the answer confidence degree and the corresponding relation between the grading data corresponding to the answer promotion degree and the questionnaire questions in the questionnaire question set, inputting the question association table into a data dimension reduction model, and comprehensively evaluating the question association table through a data dimension reduction model to obtain the evaluation information corresponding to each questionnaire question; and sequencing the questionnaire problems according to the evaluation information corresponding to each questionnaire problem to obtain the optimized questionnaire problems.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a questionnaire optimization method, apparatus, computer device, and storage medium.
Background
With the development of computer technology, computer technology is applied to various technical fields. The questionnaire survey is an information acquisition mode for rapidly acquiring useful information through limited questions, and in order to obtain enough effective data in the limited questions, principles of reasonableness, generality, logicality, clearness, non-inductivity, easy sorting and analysis, easy to go to and difficult and the like are basically required to be met during questionnaire design. Whether the designed questionnaire meets the principle or not is usually verified manually, and when the questionnaire is verified manually, the design of the current questionnaire sequence is designed according to the experience of designers when the difficulty degrees of a plurality of questions are same, and the rationality of the design of the questionnaire is difficult to ensure due to the fact that the experiences of the designers are uneven.
Disclosure of Invention
In order to solve the technical problem, the present application provides a questionnaire optimization method, apparatus, computer device, and storage medium that can improve the rationality of the questionnaire.
In one embodiment, there is provided a questionnaire optimization method, comprising:
acquiring a questionnaire answer data set and a corresponding questionnaire question set;
inputting the questionnaire answer data set into a correlation rule calculation model, and calculating the correlation degree among questionnaire answer data in the questionnaire answer data set through the correlation rule calculation model to obtain an answer correlation table, wherein the answer correlation table comprises the correlation answers, the correlated answers, answer support degrees, answer confidence degrees and grading data corresponding to the answer promotion degrees;
constructing a question association table according to the corresponding relation between scoring data corresponding to the answer association table containing associated answers, answer support degrees, answer confidence degrees and answer promotion degrees and questionnaire questions in the questionnaire question set, wherein the question association table contains scoring data corresponding to the associated questions, the number of the associated questions, the question support degrees, the question confidence degrees and the question promotion degrees;
inputting the question association table into a data dimension reduction model, and performing comprehensive evaluation on the question association table through the data dimension reduction model to obtain evaluation information corresponding to each questionnaire question;
and sequencing the questionnaire problems according to the evaluation information corresponding to each questionnaire problem to obtain the optimized questionnaire problems.
In one embodiment, there is provided a questionnaire optimizing device comprising:
the data acquisition module is used for acquiring a questionnaire answer data set and a corresponding questionnaire question set;
the answer association table building module is used for inputting the questionnaire answer data set into the association rule calculation model, calculating the association degree among the questionnaire answer data in the questionnaire answer data set through the association rule calculation model to obtain an answer association table, and the answer association table comprises associated answers, answer support degrees, answer confidence degrees and grading data corresponding to answer promotion degrees;
the question association table building module is used for building a question association table by the corresponding relation between the scoring data corresponding to the answer association table containing the associated answers, the answer support degree, the answer confidence degree and the answer promotion degree and the questionnaire questions in the questionnaire question set, wherein the question association table contains the associated questions, the number of the associated questions, the question support degree, the question confidence degree and the scoring data corresponding to the question promotion degree;
the evaluation module is used for inputting the question association table into the data dimension reduction model, and comprehensively evaluating the question association table through the data dimension reduction model to obtain evaluation information corresponding to each questionnaire question;
and the optimization module is used for sequencing the questionnaire problems according to the evaluation information corresponding to each questionnaire problem to obtain the optimized questionnaire problems.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
acquiring a questionnaire answer data set and a corresponding questionnaire question set;
inputting the questionnaire answer data set into a correlation rule calculation model, and calculating the correlation degree among questionnaire answer data in the questionnaire answer data set through the correlation rule calculation model to obtain an answer correlation table, wherein the answer correlation table comprises the correlation answers, the correlated answers, answer support degrees, answer confidence degrees and grading data corresponding to the answer promotion degrees;
constructing a question association table according to the corresponding relation between the scoring data corresponding to the associated answers, the answer support degree, the answer confidence degrees and the answer promotion degrees contained in the answer association table and the questionnaire questions in the questionnaire question set, wherein the question association table contains the scoring data corresponding to the associated questions, the number of the associated questions, the question support degree, the question confidence degrees and the question promotion degrees;
inputting the question association table into a data dimension reduction model, and performing comprehensive evaluation on the question association table through the data dimension reduction model to obtain evaluation information corresponding to each questionnaire question;
and sequencing the questionnaire problems according to the evaluation information corresponding to each questionnaire problem to obtain the optimized questionnaire problems.
A computer-readable storage medium having stored thereon a computer program, the computer program being executed by a processor for:
acquiring a questionnaire answer data set and a corresponding questionnaire question set;
inputting the questionnaire answer data set into a correlation rule calculation model, and calculating the correlation degree among questionnaire answer data in the questionnaire answer data set through the correlation rule calculation model to obtain an answer correlation table, wherein the answer correlation table comprises the correlation answers, the correlated answers, answer support degrees, answer confidence degrees and grading data corresponding to the answer promotion degrees;
constructing a question association table according to the corresponding relation between the scoring data corresponding to the associated answers, the answer support degree, the answer confidence degrees and the answer promotion degrees contained in the answer association table and the questionnaire questions in the questionnaire question set, wherein the question association table contains the scoring data corresponding to the associated questions, the number of the associated questions, the question support degree, the question confidence degrees and the question promotion degrees;
inputting the question association table into a data dimension reduction model, and performing comprehensive evaluation on the question association table through the data dimension reduction model to obtain evaluation information corresponding to each questionnaire question;
and sequencing the questionnaire problems according to the evaluation information corresponding to each questionnaire problem to obtain the optimized questionnaire problems.
The questionnaire optimization method, the device, the computer equipment and the storage medium are characterized in that a questionnaire answer data set and a corresponding questionnaire question set are obtained, the questionnaire answer data set is input into an association rule calculation model, association degrees among questionnaire answer data in the questionnaire answer data set are calculated through the association rule calculation model to obtain an answer association table, the answer association table comprises associated answers, answer support degrees, answer confidence degrees and score data corresponding to the answer promotion degrees, a question association table is constructed according to the association answers, the associated answers, the answer support degrees, the answer confidence degrees and the score data corresponding to the answer promotion degrees in the answer association table and the corresponding relations of the questionnaire questions in the questionnaire question set, and the question association table comprises associated questions, the number of the associated questions, the question support degrees, the question confidence degrees and the score data corresponding to the question promotion degrees, inputting the question association table into a data dimension reduction model, carrying out comprehensive evaluation on the question association table through the data dimension reduction model to obtain evaluation information corresponding to each questionnaire question, and sequencing the questionnaire questions according to the evaluation information corresponding to each questionnaire question to obtain the optimized questionnaire questions. The relevance between answers of the questionnaires is calculated through a relevance rule calculation model to obtain a relevance relation table between the answers, the relevance relation table between the questions is built according to the relevance relation table of the answers, a data dimension reduction model is adopted to screen questionnaire data to obtain the higher evaluation value, the answer which shows that the current question can be obtained through reasoning of the front-end question, and the design sequence of the questionnaires is determined according to the evaluation value, so that the questionnaires are optimized, and the rationality of the questionnaires is improved.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
FIG. 1 is a diagram of an application scenario of a questionnaire optimization method in an embodiment;
FIG. 2 is a schematic flow chart diagram of a method for questionnaire optimization in one embodiment;
FIG. 3 is a flowchart illustrating the step of constructing an answer association table according to an embodiment;
FIG. 4 is a flowchart illustrating a problem association table construction step in one embodiment;
FIG. 5 is a flow diagram illustrating a problem jump step in one embodiment;
FIG. 6 is a block diagram showing the structure of a questionnaire optimizing apparatus in one embodiment;
FIG. 7 is a block diagram of an answer relevance table building module in accordance with one embodiment;
FIG. 8 is a block diagram of an evaluation module in one embodiment;
FIG. 9 is a block diagram of the structure of an optimization module in one embodiment;
FIG. 10 is a block diagram that illustrates the internal architecture of the computing device, in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
FIG. 1 is a diagram of an application environment of the method for questionnaire optimization in an embodiment. Referring to fig. 1, the questionnaire optimization method is applied to a questionnaire optimization system. The questionnaire optimization system includes terminal 110 and server 120. The terminal 110 and the server 120 are connected through a network. The server 120 obtains a questionnaire answer data set and a corresponding questionnaire question set uploaded by each terminal 110, inputs the questionnaire answer data set into a correlation rule calculation model, calculates a correlation degree between questionnaire answer data in the questionnaire answer data set through the correlation rule calculation model to obtain an answer correlation table, wherein the answer correlation table comprises correlation answers, correlated answers, answer support degrees, answer confidence degrees and score data corresponding to the answer lift degrees, and a question correlation table is constructed by corresponding relations between the question questions in the questionnaire question set and the question correlation table comprises correlation questions, the number of the correlated questions, the question support degrees, the question confidence degrees and the score data corresponding to the question lift degrees, inputting the question association table into a data dimension reduction model, carrying out comprehensive evaluation on the question association table through the data dimension reduction model to obtain evaluation information corresponding to each questionnaire question, sequencing the questionnaire questions according to the evaluation information corresponding to each questionnaire question to obtain optimized questionnaire questions, and displaying the questionnaire questions on a terminal according to the optimized sequence of the questionnaire. The terminal 110 may specifically be a desktop terminal or a mobile terminal, and the mobile terminal may specifically be at least one of a mobile phone, a tablet computer, a notebook computer, and the like. The server 120 may be implemented as a stand-alone server or a server cluster composed of a plurality of servers.
In one embodiment, as shown in FIG. 2, a questionnaire optimization method is provided. The embodiment is mainly illustrated by applying the method to the terminal 110 (or the server 120) in fig. 1. Referring to fig. 2, the questionnaire optimization method specifically includes the following steps:
step S202, a questionnaire answer data set and a corresponding questionnaire question set are obtained.
Specifically, the questionnaire answer data set is answer data of each questionnaire obtained after a plurality of users answer the same set of questionnaire, and the questionnaire question set is a set formed by all questions in the same set of questionnaire. The questionnaire answer data set and the corresponding questionnaire question set are obtained by a terminal or a server. For example, when the questionnaire is an online questionnaire, the server may be used to obtain the data, that is, the server is used to obtain the questionnaire answer data set and the questionnaire question set uploaded by each terminal.
Step S204, inputting the questionnaire answer data set into the association rule calculation model, and calculating the association degree among the questionnaire answer data in the questionnaire answer data set through the association rule calculation model to obtain an answer association table.
In an embodiment of the present application, the answer association table includes scoring data corresponding to associated answers, answer support degrees, answer confidence degrees and answer elevation degrees.
Specifically, the association rule calculation model refers to a calculation model determined according to an association rule algorithm, and mining data through association rules is to mine an answer data set on the basis that technicians give "minimum support degree" and "minimum confidence degree" so as to find the strongest association rule meeting the minimum support degree and the minimum confidence degree. Association rule mining includes single-dimensional boolean association rule analysis, multi-tiered association rule mining, and constraint-based association rule mining. The core idea of the algorithm is to firstly find a frequent item set from a questionnaire answer set, and an association table can be generated when the frequent item set meets the conditions of minimum support and minimum confidence degree. The questionnaire answer data set D has a support degree S, that is, at least S% of the transactions in the questionnaire answer data set D contain X ═ Y, and is described as supported (X ═ Y) ═ P (X ═ Y). For example, support (X ═ Y) — the number of users who answer X and Y simultaneously + the total number of users. Meanwhile, the questionnaire answer data set D has a confidence C, that is, at least C% of the transactions in D that contain X also contain Y, which is described as confidence (X ═ Y) ═ P (Y | X). For example, the answer contains X, and the answer contains Y confidence level, and the confidence (X ═ Y) — the number of users whose answer contains X and Y + the number of users whose answer contains X. The degree of lift is an index that measures whether a rule is available or not, and describes how much a rule can be increased by using the rule relative to an unused rule, and the degree of lift of the useful rule is greater than 1, where the formula ═ lift ({ X → Y })/support (Y) is calculated.
Taking Apriori's algorithm as an example, if a set of terms is frequent in the questionnaire answer data set D, then all non-empty subsets thereof must also be frequent. Conversely, if a set of items is infrequent, then all of its supersets must be infrequent. If the item set A does not meet the minimum support threshold s, then A is infrequent. If b is added to A, then the support of the result item set { A, b } is certainly less than that of A, and { A, b } is also infrequent.
If k-1 is a frequent item set, i1And i2Is the same, then i1And i2Concatenating to produce new item sets Ck。CkIs IkIn which IkScanning C for combinations of data item compositionskThe count of each item set in the total things database is used for calculating the support degree, if the support degree is larger than the threshold value, the item set which does not meet the condition is a superset, and the item set which does not meet the condition is discarded. Where the number of sets of terms that may be generated is too large, resulting in too large a computation, the Apriori property may be used at this step, i.e. if the subset k-1 of the set of k terms is not in the frequent k-1 set, then the candidate will certainly not be frequent and is deleted directly. The specific execution steps of the algorithm are as follows:
inputting: an object database D and a minimum support threshold;
the operation steps are as follows: scanning the database to generate C1A set of items; count C1Item set, pruning C1Item set, get frequent I1A set of items; according to frequency I1The item sets are joined to produce candidates C2A set of items; scanning the database, discarding C2Fraction of under-counting results I2(ii) a Using Apriori properties, C is generated and simultaneously pruned (pruning infrequently)3(ii) a Scanning the database, discarding C3Fraction of under-counting results I3(ii) a Repeating the above steps to obtain the scanned item set CkAnd outputting a frequent item set.
In one embodiment, the questionnaire answer data set is preprocessed before being input into the association rule calculation model, wherein the preprocessing comprises discretization of continuous data, homogeneous data integration and the like. Discretization refers to dividing data into regions, for example, discretizing answers with continuous answer results, for example, when an age question is answered, the age can be segmented, for example, after 60 and 70, or 0 to 20 and 20 to 45 years, or further, divided into children, teenagers, middle-aged people, and the like. The homogeneous data integration refers to the integration of data with similar answers, and if the question is whether the eyes interfere, the method comprises four answer options: none, sometimes, often, always, then none and sometimes may be classified as a class, often and always as a class.
In one embodiment, the answer association table is subjected to deduplication processing, wherein the deduplication principle can be customized according to requirements, for example, deduplication is performed according to at least one deduplication principle, such as similarity of answers or conformity with topics of questionnaires. Since the frequent itemset in the key rule contains four cases: 1 pushing 1, 1 pushing more and 1 pushing more. In this situation, only push 1 and push 1 meet our needs, and finally, the updating of the questionnaire is bound to be performed on a specific subject of the questionnaire, for example, one set of questionnaire has 40 subjects, which can be regarded as a right-handed subject of frequent items, and may be about 20 subjects, but the number of the conditional frequent items may be hundreds or thousands, and the frequent items need to be deduplicated to obtain which subjects can be considered for updating.
In step S206, the answer association table includes the associated answers, the answer support degree, the answer confidence degree, and the corresponding relationship between the scoring data corresponding to the answer boost degree and the questionnaire questions in the questionnaire question set to construct the question association table.
In the embodiment of the present application, the problem association table includes associated problems, the number of the associated problems, the problem support degree, the problem confidence degree, and the score data corresponding to the problem promotion degree.
And S208, inputting the question association table into a data dimension reduction model, and comprehensively evaluating the question association table through the data dimension reduction model to obtain evaluation information corresponding to each questionnaire question.
Step S210, the questionnaire questions are sequenced according to the evaluation information corresponding to each questionnaire question, and the optimized questionnaire questions are obtained.
Specifically, after the answer association table is obtained, the question association table is determined according to the answer association table and the corresponding relationship between the questionnaire answer data set and the questionnaire question set. The question association table is obtained by counting the answer association table. And inputting the problem association table into a data dimension reduction model, and performing dimension reduction processing through the data dimension reduction model, wherein the data dimension reduction model can adopt methods for performing data dimension reduction, such as a principal component analysis method, a variation coefficient method, an entropy increase method and the like. The principal component analysis method is a method for mathematically reducing the dimension of data. The basic idea is to try toA plurality of original indexes X with certain correlation1，X2，…，Xp(e.g., P indicators) are recombined into a set of a smaller number of uncorrelated composite indicators FmTo replace the original index. How the comprehensive index should be extracted can make it reflect the original variable X to the maximum extentpThe represented information can ensure that the new indexes are kept independent of each other (the information is not overlapped).
Let F1Representing principal component indices formed by the first linear combination of the original variables, i.e. F1＝a11X1+a21X2+...+ap1XpAs known from the mathematical knowledge, the amount of information extracted from each principal component can be measured by its variance, Var (a)1) Larger, denotes F1The more information that is contained. It is often desirable to have the first principal component F1The maximum amount of information contained, and therefore F, selected among all linear combinations1Should be X1，X2，...，XpIs the largest among all linear combinations of (1), so called F1Is the first main component. If the first principal component is not enough to represent the original P indexes, then consider selecting the second principal component index F2To effectively reflect the original information, F1The existing information does not need to be presented in F2In (i) F2And F1To remain independent and uncorrelated, the covariance cover (F) is expressed in mathematical language1，F2) Not equal to 0, so F2Is a reaction of with F1Uncorrelated X1，X2，...，XpIs the largest among all linear combinations of (1), so called F2F constructed as the second principal component, and so on1、F2、...、FmIs an index X of a primary variable1，X2，...，XpFirst, second, … …, m-th principal component, all of which are specifically expressed as shown in formula (1):
and obtaining main data of the question association table through the data dimension reduction model, wherein the obtained main data can describe the whole question association table for description, evaluating the main data to obtain grading information of each data in the main data, and sequencing the questionnaire questions according to the evaluating information. The stronger the correlation with the preceding question in the case of performing the questionnaire ranking, the more suitable it is to be put at the end of the questionnaire and the more highly correlated question can be ignored as it is.
According to the questionnaire optimization method, a questionnaire answer data set and a corresponding questionnaire question set are obtained, the questionnaire answer data set is input into an association rule calculation model, association degrees among questionnaire answer data in the questionnaire answer data set are calculated through the association rule calculation model to obtain an answer association table, a question association table is constructed according to corresponding relations between score data corresponding to answer association items, answer associated items, answer support degrees, answer confidence degrees and answer promotion degrees and questionnaire questions, the question association table is input into a data dimension reduction model, comprehensive evaluation is conducted on the question association table through the data dimension reduction model to obtain evaluation information corresponding to the questionnaire questions, and the questionnaire questions are ranked according to the evaluation information corresponding to the questionnaire questions to obtain optimized questionnaire questions. Extracting data indexes by using an answer association table mined by association rules, establishing secondary indexes, modeling the data mined by the association rules and the converted secondary indexes by using a principal component analysis method, generating a scoring system aiming at questionnaire options and question contents, and sequencing the question settings of the questionnaire based on the scoring indexes obtained by principal component analysis, thereby achieving the purpose of optimizing the questionnaire structure. After the optimized questionnaire is issued, the user can judge whether a subsequent question or a plurality of questions is necessary or not at any time according to the questions answered before when answering.
In one embodiment, as shown in fig. 3, step S204 includes:
step S2042, carrying out data statistics on each questionnaire answer data in the questionnaire answer data set through the association rule calculation model to obtain corresponding statistical probability.
Step S2044, determining the answer data of each questionnaire as the associated answer or the associated answer according to the statistical probability corresponding to the answer data of each questionnaire.
Step S2046, calculating score data corresponding to the support degree, the confidence degree, and the promotion degree corresponding to each associated answer and the associated answer.
Specifically, the data statistics are used for counting the association relationship between the answer data in the questionnaire answer data set. When the statistics is carried out, the association degree between answers of two different questions can be counted, or the support degree, the confidence degree and the promotion degree of the answer of another question can be obtained by deducing from the answers of a plurality of questions. And after the questionnaire answer data set is counted through the association rule, determining which questions are associated questions and which questions are associated questions according to the statistical probability obtained after counting. The association rule is an implication in the form of X → Y, where X and Y are referred to as the leader-hand-side (LHS) and the successor (RHS) of the association rule, respectively. The associated answers refer to the leader LHS in the association rules and the associated answers refer to the set of candidates RHS in the association rules. And obtaining the support degree, the confidence degree and the promotion degree between the associated answer and the associated answer through the association rule. And the scoring data of the support degree, the confidence degree and the extraction degree is obtained by calculating the statistical probability. The association rule is adopted to process the data to obtain the association between the data, so that the specific guiding significance of the adjustment of the data is achieved.
In one embodiment, as shown in fig. 4, the data dimension reduction model is a principal component analysis model, and step S208 includes:
step S2082, calculating the average confidence, the average support degree and the average lifting degree of the associated problems corresponding to the associated problems.
And S2084, updating the problem association table according to the average confidence degree, the average support degree and the average lifting degree to obtain an updated problem association table.
Step S2086, the updated problem association table is subjected to dimensionality reduction and evaluation by adopting a principal component analysis model, and evaluation information corresponding to each questionnaire problem is obtained.
Specifically, the associated problems associated with each associated problem in the associated problem table are counted to obtain the number of associated subjects, the confidence, the support degree and the lifting degree of each associated subject and the associated subject are counted, and the weighted average of the confidence, the support degree and the lifting degree is calculated to obtain the average confidence, the average support degree and the average lifting degree. Wherein the weighting coefficients can be set by self. Such as to set the respective weighting coefficients equal. And updating the problem association table to obtain a problem association table containing the association problem, the associated problem, the average confidence degree, the average support degree and the average promotion degree. And extracting principal component factors of the problem association table by a principal component analysis method, and calculating a dependent variable H according to the principal component factors, wherein the dependent variable H is evaluation information. Where the dependent variable H is a parameter for determining the order of the questionnaire questions. The complexity of data processing can be simplified by performing principal component analysis on the average data, and a comprehensive data is obtained.
In one embodiment, step S210 includes:
step S2102 is to acquire current answer data of questions that the current user has answered the optimized questionnaire questions, and adjust the loading order of unanswered questions according to the current answer data.
Specifically, the current answer data refers to question answer data corresponding to questions answered by each user, the server acquires the question answer data corresponding to the questions answered by each user, the server processes the answer data after acquiring the answer data of the users, and the loading sequence of the questions not answered in the questionnaire of the user corresponding to the current answer data is adjusted according to the processing result.
In one embodiment, as shown in fig. 5, step S2102 comprises:
step S2102a, matches the current answer data with the associated answer data in the answer association table.
In step S2102b, when the matching is successful, the appearance order of unanswered questions corresponding to the associated answers is adjusted to the end of the questionnaire or questions are directly skipped.
Specifically, after the server acquires questionnaire answer data uploaded by the user, the questionnaire answer data carries a user identifier and a user terminal identifier, the current answer data is matched with the associated answer data in the answer association table, and when the matching is successful, the answer of the current user in answering subsequent associated questions can be deduced according to the fact that the current answer data has a high probability. In the case where the probability of concluding correctness is large, associated questions that are not answered corresponding to the associated questions may be skipped between or placed at or near the end of the questionnaire. The question is updated in real time through the association table, the questionnaire sequence can be optimized, and therefore the effectiveness of data is improved.
In a specific embodiment, the questionnaire optimization method includes:
under the given support degree and confidence degree, the Apriori algorithm is used for mining association rules to obtain an association rule table of questionnaire data, wherein the association rule table comprises lhs, rhs, support (support degree), confidence (confidence degree) and lift (lift degree) fields as shown in table (1):
TABLE 1 answer Association Table
(2) Performing index statistics on each right hand after the weight removal in the association rule table obtained in the step (1), wherein the index statistics comprises the number of associated items (the weight removal lhs), the average support, the average confidence, the average lift and the total rule number;
(3) and (3) taking the field extracted in the step (2) as a factor of principal component analysis, performing principal component analysis modeling, and finally calculating a dependent variable H according to a new principal component factor. Then, the ranking is carried out according to the dependent variable H, the more the ranking of H is, the higher the relevance between the option and the previous option is, the more suitable the option is put at the end of the questionnaire to be used as the conclusion or the conjecture of the previous question of the questionnaire, and for the question with extremely strong relevance, the question can be directly selected and omitted.
(4) And (4) obtaining the optimized questioning route of the whole questionnaire according to the ranking of the dependent variable H given in the step (3).
(5) And replying the optimized questionnaire, and judging whether the user accords with the judgment of a certain preposed item (lhs) at any time when the user answers the question based on the improved answering path so as to determine whether the question needs to be presented to the user.
By adopting the questionnaire optimization method, all options and problems of the questionnaire can be covered. Compared with the correlation of manually processing the questionnaire data or analyzing the questionnaire problems pairwise, the scheme directly mines all the problems and options of the whole questionnaire data, and can directly obtain the global optimal possibility. The questionnaire optimization method can contain any type of data. The Apriori algorithm is directly aimed at the discrete data, and for the continuous data, the discrete data obtained after discretization can be used for association rule mining by the Apriori algorithm. After the optimized questionnaire is issued, the user can judge whether a subsequent certain question or a plurality of questions is necessary or not at any time when answering, and the answering efficiency of the answerer is optimized.
Fig. 2-5 are flow diagrams of a questionnaire optimization method in one embodiment. It should be understood that although the various steps in the flow charts of fig. 2-5 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-5 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 6, there is provided a questionnaire optimizing device 200, comprising:
the data obtaining module 202 is configured to obtain a questionnaire answer data set and a corresponding questionnaire question set.
The answer association table building module 204 is configured to input the questionnaire answer data set into an association rule calculation model, and calculate association degrees between questionnaire answer data in the questionnaire answer data set through the association rule calculation model to obtain an answer association table, where the answer association table includes associated answers, answer support degrees, answer confidence degrees, and score data corresponding to answer promotion degrees.
A question association table building module 206, configured to build a question association table according to a correspondence between the scoring data corresponding to the associated answers, the answer support degree, the answer confidence degree, and the answer boost degree included in the answer association table and the questionnaire questions in the questionnaire question set, where the question association table includes the scoring data corresponding to the associated questions, the number of the associated questions, the question support degree, the question confidence degree, and the question boost degree;
and the evaluation module 208 is configured to input the question association table into the data dimension reduction model, and perform comprehensive evaluation on the question association table through the data dimension reduction model to obtain evaluation information corresponding to each questionnaire question.
And the questionnaire optimizing module 210 is configured to sort the questionnaire problems according to the evaluation information corresponding to each questionnaire problem, so as to obtain optimized questionnaire problems.
In one embodiment, as shown in fig. 7, the answer association table construction module 204 includes:
and the probability statistics unit 2042 is configured to perform data statistics on each questionnaire answer data in the questionnaire answer data set through the association rule calculation model to obtain a corresponding statistical probability.
The answer classifying unit 2044 is configured to determine, according to the statistical probability corresponding to each questionnaire answer data, that each questionnaire answer data is a relevant answer or a relevant answer.
And the scoring data calculating unit 2046 is used for calculating scoring data corresponding to the support degree, the confidence degree and the promotion degree corresponding to each associated answer and the associated answer.
In one embodiment, as shown in FIG. 8, the evaluation module 208 includes:
the average data calculation unit 2082 is used for calculating the average confidence, the average support degree and the average lifting degree of the associated problems corresponding to the associated problems.
And the problem association table updating unit 2084 is used for updating the problem association table according to the average confidence degree, the average support degree and the average lifting degree to obtain an updated problem association table.
And the evaluation unit 2086 is used for performing dimension reduction and evaluation on the updated question association table by adopting the principal component analysis model to obtain evaluation information corresponding to each questionnaire question.
In one embodiment, the optimization module 210 is further configured to obtain current answer data of questions that have been answered by the current user on the optimized questionnaire questions, and adjust the loading sequence of the unanswered questions according to the current answer data.
In one embodiment, as shown in FIG. 9, the optimization module 210 includes:
a matching unit 2102 is configured to match the current answer data with the associated answer data in the answer association table.
An optimizing unit 2104 for adjusting the order of appearance of unanswered questions corresponding to the associated answers to the end of the questionnaire or skipping the questions directly when the matching is successful.
FIG. 10 is a diagram illustrating an internal structure of a computer device in one embodiment. The computer device may specifically be the terminal 110 (or the server 120) in fig. 1. As shown in fig. 10, the computer apparatus includes a processor, a memory, a network interface, an input device, and a display screen connected through a system bus. Wherein the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program that, when executed by the processor, causes the processor to implement the questionnaire optimization method. The internal memory may also have stored therein a computer program that, when executed by the processor, causes the processor to perform a questionnaire optimization method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 10 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, the questionnaire optimizing device provided in the present application can be implemented in the form of a computer program, and the computer program can be executed on a computer device as shown in fig. 10. The memory of the computer device may store various program modules constituting the questionnaire optimizing apparatus, such as a data acquisition module 202, an answer association table construction module 204, a question association table construction module 206, an evaluation module 208, and an optimization module 210 shown in fig. 6. The computer program constituted by the respective program modules causes the processor to execute the steps in the questionnaire optimization method of the embodiments of the present application described in the present specification.
For example, the computer device shown in fig. 10 may perform the acquisition of the questionnaire answer data sets and the corresponding questionnaire question sets by the data acquisition module 202 in the questionnaire optimizing device shown in fig. 6. The computer device may execute inputting the questionnaire answer data set into the association rule calculation model through the answer association table construction module 204, and calculate association degrees between questionnaire answer data in the questionnaire answer data set through the association rule calculation model to obtain an answer association table, where the answer association table includes associated answers, answer support degrees, answer confidence degrees, and score data corresponding to answer promotion degrees. The computer device may execute, by the question association table building module 206, constructing a question association table according to the correspondence between the scoring data corresponding to the associated answers, the answer support, the answer confidence and the answer boost included in the answer association table and the questionnaire questions in the questionnaire question set, where the question association table includes the scoring data corresponding to the associated questions, the number of the associated questions, the question support, the question confidence and the question boost. The computer device can input the question association table into the data dimension reduction model through the evaluation module 208, and comprehensively evaluate the question association table through the data dimension reduction model to obtain evaluation information corresponding to each questionnaire question. The computer device may perform ranking on the questionnaire questions according to the evaluation information corresponding to each questionnaire question through the optimization module 210, so as to obtain optimized questionnaire questions.
In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program: obtaining a questionnaire answer data set and a corresponding questionnaire question set, inputting the questionnaire answer data set into an association rule calculation model, calculating association degrees among questionnaire answer data in the questionnaire answer data set through the association rule calculation model to obtain an answer association table, wherein the answer association table comprises associated answers, answer support degrees, answer confidence degrees and score data corresponding to the answer promotion degrees, a question association table is constructed according to the correspondence between the score data corresponding to the associated answers, the answer support degrees, the answer confidence degrees and the answer promotion degrees contained in the answer association table and the questionnaire questions in the questionnaire question set, the question association table comprises the number of the associated questions, the question support degrees, the question confidence degrees and the score data corresponding to the question promotion degrees, and the question association table is input into a data dimension reduction model, and comprehensively evaluating the problem association table through a data dimension reduction model to obtain evaluation information corresponding to each questionnaire problem, and sequencing the questionnaire problems according to the evaluation information corresponding to each questionnaire problem to obtain the optimized questionnaire problems.
In one embodiment, calculating the association degree between the answer data of each questionnaire in the answer data set of questionnaires through an association rule calculation model to obtain an answer association table includes: and performing data statistics on each questionnaire answer data in the questionnaire answer data set through an association rule calculation model to obtain corresponding statistical probability, determining each questionnaire answer data as an associated answer or an associated answer according to the statistical probability corresponding to each questionnaire answer data, and calculating grading data corresponding to the support degree, the confidence degree and the promotion degree corresponding to each associated answer and the associated answer.
In one embodiment, the data dimension reduction model dimension is a principal component analysis model, the question association table is input into the data dimension reduction model, the comprehensive evaluation is performed on the question association table through the data dimension reduction model, and the obtaining of the evaluation information corresponding to each questionnaire question includes: calculating the average confidence, the average support and the average lifting degree of the associated questions corresponding to the associated questions, updating the question association table according to the average confidence, the average support and the average lifting degree to obtain an updated question association table, and performing dimensionality reduction and evaluation on the updated question association table by adopting a principal component analysis model to obtain evaluation information corresponding to the questionnaire questions.
In one embodiment, ranking the questionnaire questions according to the evaluation information corresponding to each questionnaire question includes: and acquiring current answer data of questions answered by the optimized questionnaire questions by the current user, and adjusting the loading sequence of the unanswered questions according to the current answer data.
In one embodiment, determining the loading order of unanswered questions from the current answer data comprises: and matching the current answer data with the associated answer data in the answer association table, and when the matching is successful, adjusting the appearance sequence of the unanswered questions corresponding to the associated answers to the end of the questionnaire or directly skipping the questions.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of: obtaining a questionnaire answer data set and a corresponding questionnaire question set, inputting the questionnaire answer data set into an association rule calculation model, calculating association degrees among questionnaire answer data in the questionnaire answer data set through the association rule calculation model to obtain an answer association table, wherein the answer association table comprises associated answers, answer support degrees, answer confidence degrees and score data corresponding to the answer promotion degrees, a question association table is constructed according to the correspondence between the score data corresponding to the associated answers, the answer support degrees, the answer confidence degrees and the answer promotion degrees contained in the answer association table and the questionnaire questions in the questionnaire question set, the question association table comprises the number of the associated questions, the question support degrees, the question confidence degrees and the score data corresponding to the question promotion degrees, and the question association table is input into a data dimension reduction model, and comprehensively evaluating the problem association table through a data dimension reduction model to obtain evaluation information corresponding to each questionnaire problem, and sequencing the questionnaire problems according to the evaluation information corresponding to each questionnaire problem to obtain the optimized questionnaire problems.
In one embodiment, calculating the association degree between the answer data of each questionnaire in the answer data set of questionnaires through an association rule calculation model to obtain an answer association table includes: and performing data statistics on each questionnaire answer data in the questionnaire answer data set through an association rule calculation model to obtain corresponding statistical probability, determining each questionnaire answer data as an associated answer or an associated answer according to the statistical probability corresponding to each questionnaire answer data, and calculating grading data corresponding to the support degree, the confidence degree and the promotion degree corresponding to each associated answer and the associated answer.
In one embodiment, the data dimension reduction model dimension is a principal component analysis model, the question association table is input into the data dimension reduction model, the comprehensive evaluation is performed on the question association table through the data dimension reduction model, and the obtaining of the evaluation information corresponding to each questionnaire question includes: calculating the average confidence, the average support and the average lifting degree of the associated questions corresponding to the associated questions, updating the question association table according to the average confidence, the average support and the average lifting degree to obtain an updated question association table, and performing dimensionality reduction and evaluation on the updated question association table by adopting a principal component analysis model to obtain evaluation information corresponding to the questionnaire questions.
In one embodiment, ranking the questionnaire questions according to the evaluation information corresponding to each questionnaire question includes: and acquiring current answer data of questions answered by the optimized questionnaire questions by the current user, and adjusting the loading sequence of the unanswered questions according to the current answer data.
In one embodiment, determining the loading order of unanswered questions from the current answer data comprises: and matching the current answer data with the associated answer data in the answer association table, and when the matching is successful, adjusting the appearance sequence of the unanswered questions corresponding to the associated answers to the end of the questionnaire or directly skipping the questions.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A questionnaire optimization method, the method comprising:
acquiring a questionnaire answer data set and a corresponding questionnaire question set;
inputting the questionnaire answer data set into a correlation rule calculation model, and calculating the correlation degree between questionnaire answer data in the questionnaire answer data set through the correlation rule calculation model to obtain an answer correlation table, wherein the answer correlation table comprises score data corresponding to answer support degrees respectively corresponding to correlation answers and correlated answers, score data corresponding to answer confidence degrees and score data corresponding to answer promotion degrees; wherein the associated answer is a leader in the association rule, and the associated answer is a successor in the association rule; the scoring data corresponding to the answer support degree, the scoring data corresponding to the answer confidence degree and the scoring data corresponding to the answer promotion degree are obtained through statistical probability calculation;
according to the grading data corresponding to the answer support degrees respectively corresponding to the associated answers and the associated answers, the grading data corresponding to the answer confidence degrees and the grading data corresponding to the answer promotion degrees contained in the answer association table, constructing a question association table according to the corresponding relation of the question in the questionnaire question set, wherein the question association table contains the grading data corresponding to the question support degrees respectively corresponding to the associated questions and the associated questions, the grading data corresponding to the question confidence degrees and the grading data corresponding to the question promotion degrees;
inputting the question association table into a data dimension reduction model, and performing comprehensive evaluation on the question association table through the data dimension reduction model to obtain evaluation information corresponding to each questionnaire question;
and sequencing the questionnaire problems according to the evaluation information corresponding to each questionnaire problem to obtain the optimized questionnaire problems.
2. The method according to claim 1, wherein the calculating, by the association rule calculation model, the association degree between the answer data of each questionnaire in the answer data set of questionnaires to obtain an answer association table includes:
performing data statistics on each questionnaire answer data in the questionnaire answer data set through the association rule calculation model to obtain corresponding statistical probability;
determining the answer data of each questionnaire as a related answer or a related answer according to the corresponding statistical probability of the answer data of each questionnaire;
and calculating the grading data corresponding to the support degree, the confidence degree and the promotion degree of each associated answer and the associated answer.
3. The method according to claim 1, wherein the data dimension reduction model is a principal component analysis model, the inputting of the question association table into the data dimension reduction model and the comprehensive evaluation of the question association table by the data dimension reduction model to obtain the evaluation information corresponding to each questionnaire question comprises:
calculating the average confidence, the average support degree and the average lifting degree of the associated problems corresponding to the associated problems;
updating the problem association table according to the average confidence, the average support and the average promotion to obtain the updated problem association table;
and adopting the principal component analysis model to perform dimensionality reduction and evaluation on the updated question association table to obtain evaluation information corresponding to each questionnaire question.
4. The method according to claim 1, wherein the ranking the questionnaire questions according to the evaluation information corresponding to each questionnaire question comprises:
obtaining current answer data of questions answered by the current user to the optimized questionnaire questions;
and adjusting the loading sequence of the unanswered questions according to the current answer data.
5. The method of claim 4, wherein determining the loading order of unanswered questions from the current answer data comprises:
matching the current answer data with the associated answer data in the answer association table;
when the matching is successful, adjusting the appearance sequence of the unanswered questions corresponding to the associated answers to the end of the questionnaire or skipping the questions directly.
6. A questionnaire optimizing device, characterized in that the device comprises:
the data acquisition module is used for acquiring a questionnaire answer data set and a corresponding questionnaire question set;
the answer association table building module is used for inputting the questionnaire answer data set into an association rule calculation model, calculating association degrees among questionnaire answer data in the questionnaire answer data set through the association rule calculation model, and obtaining an answer association table, wherein the answer association table comprises score data corresponding to answer support degrees respectively corresponding to associated answers and associated answers, score data corresponding to answer confidence degrees and score data corresponding to answer promotion degrees; wherein the associated answer is a leader in the association rule, and the associated answer is a successor in the association rule; the scoring data corresponding to the answer support degree, the scoring data corresponding to the answer confidence degree and the scoring data corresponding to the answer promotion degree are obtained through statistical probability calculation;
a question association table building module, configured to build a question association table according to the corresponding relationship between the question association table and the questionnaire questions in the questionnaire question set, where the question association table includes score data corresponding to answer support degrees respectively corresponding to associated answers and the associated answers, score data corresponding to answer confidence degrees, and score data corresponding to answer boost degrees, and the question association table includes score data corresponding to question support degrees respectively corresponding to associated questions and the associated questions, score data corresponding to question confidence degrees, and score data corresponding to question boost degrees;
the evaluation module is used for inputting the question association table into a data dimension reduction model, and comprehensively evaluating the question association table through the data dimension reduction model to obtain evaluation information corresponding to each questionnaire question;
and the questionnaire optimization module is used for sequencing the questionnaire problems according to the evaluation information corresponding to each questionnaire problem to obtain the optimized questionnaire problems.
7. The apparatus of claim 6, wherein the answer association table construction module comprises:
the probability statistical unit is used for carrying out data statistics on each questionnaire answer data in the questionnaire answer data set through the association rule calculation model to obtain corresponding statistical probability;
the answer classification unit is used for determining each questionnaire answer data as a related answer or a related answer according to the statistical probability corresponding to each questionnaire answer data;
and the scoring data calculating unit is used for calculating scoring data corresponding to the support degree, the confidence degree and the promotion degree corresponding to each associated answer and the associated answer.
8. The apparatus of claim 6, wherein the evaluation module comprises:
the average data calculation unit is used for calculating the average confidence degree, the average support degree and the average lifting degree of the associated problems corresponding to the associated problems;
the problem association table updating unit is used for updating the problem association table according to the average confidence degree, the average support degree and the average promotion degree to obtain the updated problem association table;
and the evaluation unit is used for performing dimensionality reduction and evaluation on the updated question association table by adopting a principal component analysis model to obtain evaluation information corresponding to each questionnaire question.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1 to 5 are implemented when the computer program is executed by the processor.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 5.
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