CN109190051B - User behavior analysis method and resource recommendation method based on analysis method - Google Patents
User behavior analysis method and resource recommendation method based on analysis method Download PDFInfo
- Publication number
- CN109190051B CN109190051B CN201811447333.3A CN201811447333A CN109190051B CN 109190051 B CN109190051 B CN 109190051B CN 201811447333 A CN201811447333 A CN 201811447333A CN 109190051 B CN109190051 B CN 109190051B
- Authority
- CN
- China
- Prior art keywords
- user
- behavior
- resource
- demand
- graph
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention relates to a user behavior analysis method and a resource recommendation method based on the same, wherein the user behavior analysis method comprises the following steps: generating a directed behavior graph according to behavior data of a certain user; calculating the behavior edge weight of the directed behavior graph, and extracting an effective path; dividing the behavior data into directional requirements and fuzzy requirements; placing the directional demand characteristics of a plurality of users into a user demand relational database; clustering analysis is carried out on the fuzzy demand characteristics of a plurality of similar users and/or the similar fuzzy demand characteristics of a plurality of users, and the obtained similar user demand characteristic set and/or the similar demand user characteristic set are/is put into a user demand relational database; generating a resource role frame according to the directed behavior diagram; and generating a decision tree according to the resource role framework and the user demand relation library. The resource recommendation method comprises the following steps: and acquiring the fuzzy demand characteristics of the user, and recommending resources to the user according to the decision tree. The invention can reduce the difficulty of behavior analysis of the new user and excavate the fuzzy requirement of the new user.
Description
Technical Field
The invention relates to the technical field of information classification processing, in particular to a user behavior analysis method and a resource recommendation method based on the analysis method.
Background
The conversion of scientific and technical achievements is always a difficult problem for scientific and technical researchers, and meanwhile, product producers also spend a lot of cost to put into technical research due to the lack of technology, how to butt joint the existing scientific and technical resources with the production requirements, and how to convert the scientific and technical resources into beneficial achievements to the maximum becomes a problem existing in the current scientific and technical research.
In the scientific and technological resource supply and demand docking, the scientific and technological achievements of the supplier and the demander are huge, and for the supplier, the scientific and technological resource data are detailed and huge, so that the scientific and technological achievement display is clear and clear, but the output of the scientific and technological achievement is weak, which is determined by the scientific and technological resource supply and demand docking mode. In most cases, the requirements of both the supply and demand sides of the scientific and technological resources cannot be matched. The main reason is that the information owned by the supplier and the demander is not equal, on one hand, the supplier cannot subdivide the occupied scientific and technological resources according to the market demand and cannot quickly know the desire of the demander; on the other hand, the demander does not describe his own requirements exhaustively or the described characteristics of the requirements differ considerably from the supplier's conception. This makes it extremely difficult to interface the scientific resources of both the supply and demand parties. When the supply and demand parties complete the result of sufficient preparation work, the scientific and technological resource butt joint can be completed, and therefore the supply and demand butt joint efficiency of the scientific and technological resources is greatly reduced. Even if insufficient preparation work is available, the demander needs to search and research for many times to know the information of the wanted supplier, meanwhile, in the continuous search of the demander, the searching formula used by the demander is provided by the supplier, the searching formula is not fine enough, meanwhile, the index construction does not comply with the will of the demander, and the demander is not friendly, so that the supply and demand docking of the scientific resources is not convenient.
Disclosure of Invention
The invention aims to overcome at least one defect (deficiency) of the prior art, and provides a user behavior analysis method and a resource recommendation method based on the analysis method, which can well analyze user behaviors, reduce the difficulty of analyzing new user behaviors according to a decision tree generated by analysis, and mine the fuzzy requirement of a new user.
The technical scheme adopted by the invention is as follows:
a user behavior analysis method, comprising:
generating a directed behavior graph according to behavior data of a certain user;
calculating the behavior edge weight of the directed behavior graph, and extracting an effective path;
dividing the behavior data into directional requirements and fuzzy requirements, forming directional requirement characteristics by the mapping relation between the resource characteristics corresponding to the directional requirements and the user characteristics of the user, forming fuzzy requirement characteristics by the mapping relation between the resource characteristics corresponding to the fuzzy requirements and the user characteristics of the user, and putting the directional requirement characteristics of a plurality of users into a user requirement relation library;
performing cluster analysis on the fuzzy demand characteristics of a plurality of similar users to obtain a similar user demand characteristic set and/or performing cluster analysis on the similar fuzzy demand characteristics of a plurality of users to obtain a similar demand user characteristic set, and putting the similar user demand characteristic set and/or the similar demand user characteristic set into a user demand relation library;
generating a resource role frame according to the directed behavior diagram;
and generating a decision tree according to the resource role framework and the user demand relation library.
The method comprises the steps of sampling a plurality of user behavior data, dividing the sampled behavior data into a directional demand and a fuzzy demand, respectively analyzing and processing the directional demand and the fuzzy demand to generate a resource role framework and a user demand relation library, and constructing a decision tree. The requirements of the users are analyzed through the decision tree, the difficulty of requirement analysis can be reduced, and the fuzzy requirements of the users are fully mined, so that resources and services which are more in line with the requirements of the users can be recommended for the users.
Further, generating a directed behavior graph according to behavior data of a certain user specifically includes:
recording the transaction type, operation type, action time length and resource characteristics of a certain user action;
forming a growth tree by taking the transaction type as a root node and the operation type as a child node;
establishing a cell pool on the child node according to the action time length, wherein the resource characteristics are used as cells in the cell pool;
and generating a directed behavior graph by taking the cell pool as an edge.
Further, generating a directed behavior graph according to behavior data of a certain user specifically includes:
judging whether a user is a directional demand user or a fuzzy demand user according to behavior data of the user;
if the user is the directional demand user, generating a directed behavior graph according to the behavior data of the user;
and if the user is the fuzzy demand user, generating a directed behavior graph according to the behavior data of the user and the behavior data of similar users of the user.
When the behavior data of the user is recorded, different records can be carried out according to different requirements of the user, and the generated directed behavior graph can enable the user requirement relational library, the resource role framework and the decision tree which are generated subsequently to be capable of better analyzing the behavior of the user.
Further, according to behavior data of a certain user, whether the user is a directional demand user or a fuzzy demand user is judged, and the method specifically includes:
and judging whether the user is a directional demand user or a fuzzy demand user according to the behavior similarity between a behavior data structure of the user and a user behavior logic model, and/or the resource similarity between the resource characteristics in the user behavior data and the resource roles in the pre-established resource role characteristic library, and/or the user similarity between the user characteristics of the user and the user roles in the pre-established user role characteristic library.
Further, calculating the behavior edge weight of the directed behavior graph, and extracting an effective path specifically includes:
performing cluster analysis on the behavior data on a time scale, and calculating the time weight of the directed behavior graph to form a time behavior data graph;
performing clustering analysis on the behavior data on a spatial scale, and calculating the spatial weight of the directed behavior graph to form a spatial behavior data graph;
and calculating the behavior edge weight of the directed behavior graph according to the time weight and the space weight, and respectively extracting effective paths of the time behavior data graph and the space behavior data graph.
Further, dividing the behavior data into a directional requirement and a fuzzy requirement specifically includes:
combining the shortest paths of the time behavior data graph and the space behavior data graph to obtain the direct demand characteristics of the user;
analyzing the loop intersection points in the effective paths, extracting the loop-free paths, performing similarity analysis on the resource characteristics of the loop-free paths and the characteristics directly required by the user, and determining whether the behavior data corresponding to the loop-free paths is a directional requirement or a fuzzy requirement according to the similarity analysis result.
Further, generating a resource role framework according to the directed behavior graph specifically includes:
extracting edges with weights higher than a preset value in the directed behavior graph as a sub-graph a;
traversing a user demand relation library, and screening a sub-graph b with the support degree higher than a support degree threshold;
setting the resource characteristic of the sub-graph a as A and the resource characteristic of the sub-graph B as B, and calculating confidence coefficients from A to B and from B to A;
and screening out the resource features with the confidence coefficient higher than the confidence coefficient threshold value, and forming a resource role framework by the screened-out resource features.
Further, generating a decision tree according to the resource role framework and the user requirement relation library, specifically comprising:
sequencing the resource characteristics of the resource role framework according to the confidence coefficient by adopting a bubbling method, and taking the obtained sequence as a main decision rule; inducing the user requirement relation library to form an auxiliary decision rule;
and generating a decision tree according to the main decision rule and the auxiliary decision rule.
A resource recommendation method, comprising:
acquiring fuzzy demand characteristics of a user;
acquiring a demand characteristic with the highest relevance to the fuzzy demand characteristic and a resource role corresponding to the demand characteristic according to the decision tree;
and recommending resources to the user according to the resource roles.
When the fuzzy requirement is input by the user, the requirement of the user is analyzed through the decision tree, the difficulty of requirement analysis can be reduced, and the fuzzy requirement of the user is fully mined, so that resources and services which are more in line with the requirement of the user can be recommended to the user.
Further, the method further comprises:
acquiring a user role according to the user characteristics of the user;
and recommending resources to the user according to the user role and the resource role.
Compared with the prior art, the invention has the beneficial effects that: by formulating a user behavior data recording mode, generating a directed behavior graph according to user behavior data, dividing the user behavior data into directional requirements and fuzzy requirements, forming a resource role framework and a user requirement relational database, constructing a decision tree, reducing the difficulty of analyzing the new user behavior data, mining the fuzzy requirements of new users, and providing more friendly resource recommendation for the users.
Drawings
Fig. 1 is a schematic flow chart of a user behavior analysis method.
Detailed Description
The drawings are only for purposes of illustration and are not to be construed as limiting the invention. For a better understanding of the following embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
Example 1
As shown in fig. 1, the present embodiment provides a user behavior analysis method, including:
A1. generating a directed behavior graph according to behavior data of a certain user;
A2. calculating the behavior edge weight of the directed behavior graph, and extracting an effective path;
A3. dividing the behavior data into directional requirements and fuzzy requirements, forming directional requirement characteristics by the mapping relation between the resource characteristics corresponding to the directional requirements and the user characteristics of the user, forming fuzzy requirement characteristics by the mapping relation between the resource characteristics corresponding to the fuzzy requirements and the user characteristics of the user, and putting the directional requirement characteristics of a plurality of users into a user requirement relation library;
A4. performing cluster analysis on the fuzzy demand characteristics of a plurality of similar users to obtain a similar user demand characteristic set and/or performing cluster analysis on the similar fuzzy demand characteristics of a plurality of users to obtain a similar demand user characteristic set, and putting the similar user demand characteristic set and/or the similar demand user characteristic set into a user demand relation library;
A5. generating a resource role frame according to the directed behavior diagram;
A6. and generating a decision tree according to the resource role framework and the user demand relation library.
The method comprises the steps of sampling a plurality of user behavior data, dividing the sampled behavior data into a directional demand and a fuzzy demand, respectively analyzing and processing the directional demand and the fuzzy demand to generate a resource role framework and a user demand relation library, and constructing a decision tree. The requirements of the users are analyzed through the decision tree, the difficulty of requirement analysis can be reduced, and the fuzzy requirements of the users are fully mined, so that resources and services which are more in line with the requirements of the users can be recommended for the users.
Step a1 specifically includes:
A11. recording the transaction type, operation type, action time length and resource characteristics of a certain user action;
A12. forming a growth tree by taking the transaction type as a root node and the operation type as a child node;
A13. establishing a cell pool on the child node according to the action time length, wherein the resource characteristics are used as cells in the cell pool;
A14. and generating a directed behavior graph by taking the cell pool as an edge.
The transaction type is a classification of the nature of the user's behavior, such as browsing, searching, intent confirmation, consultation, transaction, etc.
The operation type is a classification of data interaction actions in user behaviors, the data interaction actions may include all keyboard and mouse operations and all data information in the behavior process, for example, when the transaction type is search, the operation type may be keyword input, backspace, delete, confirmation and the like in the search behavior process.
The starting time and the ending time of each behavior of the user are recorded to calculate the duration of the whole behavior, which is recorded as the time length of the behavior, and a point can be buried in the ending action of each behavior in the implementation process, so as to confirm that the behavior is ended. Taking the transaction type as an example for searching, after a user carries out a series of operations in a search box and a screening list, a point is buried on a 'confirm' or 'screening' key, and when the buried point is triggered, the 'searching' behavior is marked to be ended.
The resource characteristics related to each behavior of the user are recorded to form the resource characteristics.
In summary, the behavior data of the user can be recorded in the form of "transaction code + operation code + data packet + end code". When the whole behavior data is recorded, a growing tree is formed by taking a transaction type as a root node and an operation type as a child node, the behavior time length is quantized, a cell pool for data collection is set under the child node of the growing tree according to the behavior time length quantization value, and the resource characteristics are stored in the cell pool, so that a directed behavior graph with the cell pool as a side can be generated.
Step a11 specifically includes:
A111. judging whether a user is a directional demand user or a fuzzy demand user according to behavior data of the user;
A112. if the user is the directional demand user, generating a directed behavior graph according to the behavior data of the user;
A113. and if the user is the fuzzy demand user, generating a directed behavior graph according to the behavior data of the user and the behavior data of similar users of the user.
When recording the behavior data of the user, different records can be performed according to different requirements and intentions of the user. If the requirement intention degree expressed by the behavior data of the user is fuzzy requirement, the behavior data of similar users can be recorded when the behavior data of the user is recorded, and a directed behavior graph is generated according to the behavior data of the user and the similar users. The generated directed behavior graph can enable the user demand relational library, the resource role framework and the decision tree which are generated subsequently to analyze the behavior of the user better.
The step a111 specifically includes: and judging whether the user is a directional demand user or a fuzzy demand user according to the behavior similarity between a behavior data structure of the user and a user behavior logic model, and/or the resource similarity between the resource characteristics in the user behavior data and the resource roles in the pre-established resource role characteristic library, and/or the user similarity between the user characteristics of the user and the user roles in the pre-established user role characteristic library.
Carrying out similarity analysis between a behavior data structure of a user and a user behavior logic model, wherein the analyzed behavior similarity X is a user requirement intention evaluation index I; carrying out similarity analysis between the resource characteristics in the user behavior data and the resource roles in the pre-established resource role characteristic library, wherein the analyzed resource similarity Y is a demand intention evaluation index II; and analyzing the similarity between the user characteristics of the user and the user roles in the pre-established user role characteristic library, wherein the analyzed user similarity Z is an evaluation index III of the user requirement intention, and the user similarity Z is 1 when the user roles cannot be obtained. And when the evaluation indexes I, II and/or III of the user demand intention degree are/is lower than the lower limit value, judging that the user is a fuzzy demand user. When three evaluation indexes are adopted for judgment at the same time, the weight sequence of the evaluation indexes I, II and III of the user demand intention degree is preferably as follows: II > I > III.
When the user is judged to be the fuzzy demand user, recording user behavior data by adopting a rule 1 shown in fig. 1, namely when the user behavior data is recorded, recording similar user behavior data of the user, and generating a directed behavior diagram according to the behavior data of the user and the similar user; when the user is judged to be the user with the directional demand, the rule 2 shown in fig. 1 is adopted to record the user behavior data, that is, only the behavior data of the user needs to be recorded, and a directed behavior diagram is generated according to the behavior data of the user.
The behavior logic model can be an empirical model obtained by counting a large number of user behaviors; the calculation rule of the behavior logic model may also be set according to the self market research situation, and the calculation rule is exemplified as follows: if a user firstly searches for a transaction and then browses, correlation analysis is carried out on search keywords according to a user demand relation library, if the correlation degree is high, directional demand is determined, if the correlation degree is low, fuzzy demand is determined, follow-up behavior operation of the user is monitored continuously, if the record shows that when the user overviews the whole transaction in browsing, the dwell time of a page is lower than the mean dwell time of the page of the user, the head and tail resource data information appearing on the page is recorded, and the demand degree keeps unchanged until the user enters the resource page to drive the browsing transaction, and the demand degree is changed into the directional demand. In specific implementation, the demand degree may be initially set to 0, and in monitoring the subsequent behavior of the user, the demand degree may be updated according to the user behavior.
The pre-building process of the resource role feature library can be as follows: extracting resource features according to the provided resources; and performing clustering analysis on the resource characteristics to establish a resource role characteristic library. The resources may specifically be scientific and technological resources, and may include instruments, core technical theories, method systems, and the like; the resource characteristics may include a resource name, a data interface, a data supplier characteristic, a resource application object, a background to which the resource belongs, a resource utility performance characteristic, and the like. The extraction of resource features may be based on a resource semantic analysis method.
The pre-building process of the user role feature library can be as follows: extracting user characteristics according to basic data information of each user; clustering and analyzing the user characteristics to establish a user role characteristic library; the user role characteristic library comprises a user characteristic relation and a similar user resource characteristic relation. Extracting the user roles according to the basic data information of each user, which specifically may be: and analyzing the basic information of the user and the resource occupation condition of the user to obtain the user role.
Step a2 specifically includes:
A21. performing cluster analysis on the behavior data on a time scale, and calculating the time weight of the directed behavior graph to form a time behavior data graph;
A22. performing clustering analysis on the behavior data on a spatial scale, and calculating the spatial weight of the directed behavior graph to form a spatial behavior data graph;
A23. and calculating the behavior edge weight of the directed behavior graph according to the time weight and the space weight, and respectively extracting effective paths of the time behavior data graph and the space behavior data graph.
In step a21, performing cluster analysis on the resource features in the cell pool on a time scale to obtain behavior resource feature clusters in unit time, and performing feature distribution analysis on the clusters on the time axis through fourier transform to obtain feature frequency distribution, where the frequency distribution constitutes the weight of the time behavior data graph.
In step a22, performing cluster analysis on the resource features in the cell pool on a spatial scale, that is, performing cluster analysis on the resource features in the entire behavior data sample to obtain a plurality of clusters, backtracking the data in different clusters in time to obtain the distribution of similar behavior data in the behavior flow, sharpening the distribution to remove a low frequency value to obtain a high frequency distribution, analyzing the time continuity of the data features in the high frequency distribution to obtain a high frequency feature linear distribution value, and obtaining behavior resource correlation distribution according to the linear correlation value, so as to use the high frequency feature linear distribution value as the weight of the spatial behavior data graph.
In step a23, calculating the behavior edge weight of the directed behavior graph according to the temporal weight and the spatial weight specifically includes: firstly, classifying resource features in user behavior data according to the resource field and user features of users, wherein each type of resource features has corresponding weight, counting the resource features with the same transaction type, classifying the resource features according to the weights to obtain resource feature data quantity of each weight, and superposing the behavior time length, the data quantity and the weights to obtain the resource demand basic proportion of the user behavior; secondly, the resource features in the user behavior data are counted, the data features are extracted, the data features are weighted according to the basic proportion of the resource requirements, a weighted data feature probability distribution curve is obtained, the distribution lines are sharpened, and the user behavior core requirement weight, namely the behavior edge weight of the directed behavior graph, is obtained.
Step a3 specifically includes:
A31. combining the shortest paths of the time behavior data graph and the space behavior data graph to obtain the direct demand characteristics of the user;
A32. analyzing the circuit cross points in the effective paths, extracting the non-circuit paths, extracting the sides of which the cell pools are larger than the threshold value, performing similarity analysis on the resource characteristics corresponding to the extracted sides and the characteristics directly required by the user, and determining whether the behavior data corresponding to the sides is the directional requirement or the fuzzy requirement according to the similarity analysis result.
The specific implementation process of step a32 may be: analyzing a loop intersection point in an effective path, extracting a loop-free path, extracting a side with a larger cell pool, counting the weight of the side, if the weight of the side is lower than the average value of the overall weight, calling a resource feature corresponding to the side, performing similarity analysis on the resource feature and a feature directly required by a user to obtain similarity, classifying behavior data corresponding to the side with the similarity lower than the average value as fuzzy requirements, and classifying behavior data corresponding to the side with the similarity higher than the average value as directional requirements.
In step a4, performing cluster analysis on the fuzzy demand characteristics of a plurality of similar users to obtain a demand characteristic set of the similar users, which specifically includes:
A41. in a directed behavior graph generated according to similar user behavior data, acquiring an edge which describes the direct demand characteristics of similar users and has a weight higher than a weight threshold value in a shortest path as a subgraph, and calculating the support degree of the subgraph;
A42. and taking the resource features in the subgraph with the support degree higher than the support degree threshold value as the similar user requirement features to form a similar user requirement feature set.
The weight threshold may be a weight mean; the support threshold may be a support mean.
Step a5 specifically includes:
A51. extracting edges with weights higher than a preset value in the directed behavior graph as a sub-graph a;
A52. traversing a user demand relation library, and screening a sub-graph b with the support degree higher than a support degree threshold;
A53. setting the resource characteristic of the sub-graph a as A and the resource characteristic of the sub-graph B as B, and calculating confidence coefficients from A to B and from B to A; A54. and screening out the resource features with the confidence coefficient higher than the confidence coefficient threshold value, and forming a resource role framework by the screened-out resource features.
The support threshold may be a support mean; the confidence threshold may be a confidence mean.
Step a6 specifically includes:
A61. sequencing the resource characteristics of the resource role framework according to the confidence coefficient by adopting a bubbling method, and taking the obtained sequence as a main decision rule;
A62. inducing the user requirement relation library to form an auxiliary decision rule;
A63. and generating a decision tree according to the main decision rule and the auxiliary decision rule.
The auxiliary decision rule does not influence the sequence of the main decision rule and is only used for calculating the proportion of the resource requirements. The resource role funnel tree diagram is obtained according to the main decision rule, then the resource demand proportion is calculated according to the auxiliary decision rule, and the weight of the resource characteristics of each node in the resource role funnel tree diagram is adjusted according to the resource demand proportion, so that the decision tree is generated.
Example 2
The embodiment provides a resource recommendation method, which includes:
B1. acquiring fuzzy demand characteristics of a user;
B2. according to the decision tree in the embodiment 1, acquiring a demand characteristic with the highest relevance to the fuzzy demand characteristic of the user and a resource role corresponding to the demand characteristic;
B3. acquiring a corresponding user role according to the user characteristics of the user;
B4. and recommending resources to the user according to the user role and the resource role.
When the fuzzy requirement is input by the user, the requirement of the user is analyzed through the decision tree, the difficulty of requirement analysis can be reduced, and the fuzzy requirement of the user is fully mined, so that resources and services which are more in line with the requirement of the user can be recommended to the user.
The resource characteristics can be divided into primary data characteristics formed by primary attributes of resources such as resource names, resource interfaces, application objects, background information and the like, and secondary data characteristics formed by secondary attributes describing supplementary information such as resource supplier characteristics, utility performance and the like. The primary data feature corresponds to a directional requirement, such as a requirement for specific information including a resource name of a certain instrument, a certain technology, a certain patent, or the like, or information describing a specific application object or an application field. The secondary data features correspond to fuzzy requirements, such as requirements describing specific problems in processing production, actual application requirements, and the like.
When the requirement input by the user is to process specific production problems, practical application problems and the like, the fuzzy requirement characteristic is considered to be input by the user. After the fuzzy demand characteristics of the user are obtained, the secondary data characteristics are obtained and recorded as the resource characteristics C. And carrying out hidden layer analysis on the fuzzy demand characteristics according to the decision tree. Specifically, a resource feature E with the highest correlation degree with the resource feature C and a resource role corresponding to the resource feature E are extracted according to the decision tree through confidence calculation. Resources can be recommended to the user according to the resource roles, and the resources can also be recommended to the user by combining the user roles and the resource roles. The user role can be acquired through the user role feature library according to the user features of the user. The method provided by the embodiment can be used for performing directional analysis on the fuzzy demand user, and the behavior analysis difficulty of the fuzzy demand user is reduced.
It should be understood that the above-mentioned embodiments of the present invention are only examples for clearly illustrating the technical solutions of the present invention, and are not intended to limit the specific embodiments of the present invention. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention claims should be included in the protection scope of the present invention claims.
Claims (10)
1. A user behavior analysis method is characterized by comprising the following steps:
generating a directed behavior graph according to behavior data of a certain user;
calculating the behavior edge weight of the directed behavior graph, and extracting an effective path;
dividing the behavior data into directional requirements and fuzzy requirements, forming directional requirement characteristics by the mapping relation between the resource characteristics corresponding to the directional requirements and the user characteristics of the user, forming fuzzy requirement characteristics by the mapping relation between the resource characteristics corresponding to the fuzzy requirements and the user characteristics of the user, and putting the directional requirement characteristics of a plurality of users into a user requirement relation library;
performing cluster analysis on the fuzzy demand characteristics of a plurality of similar users to obtain a similar user demand characteristic set and/or performing cluster analysis on the similar fuzzy demand characteristics of a plurality of users to obtain a similar demand user characteristic set, and putting the similar user demand characteristic set and/or the similar demand user characteristic set into a user demand relation library;
generating a resource role frame according to the directed behavior diagram;
and generating a decision tree according to the resource role framework and the user demand relation library.
2. The method for analyzing user behavior according to claim 1, wherein generating a directed behavior graph according to behavior data of a certain user specifically includes:
recording the transaction type, operation type, action time length and resource characteristics of a certain user action;
forming a growth tree by taking the transaction type as a root node and the operation type as a child node;
establishing a cell pool on the child node according to the action time length, wherein the resource characteristics are used as cells in the cell pool;
and generating a directed behavior graph by taking the cell pool as an edge.
3. The method for analyzing user behavior according to claim 1, wherein generating a directed behavior graph according to behavior data of a certain user specifically includes:
judging whether a user is a directional demand user or a fuzzy demand user according to behavior data of the user;
if the user is the directional demand user, generating a directed behavior graph according to the behavior data of the user;
and if the user is the fuzzy demand user, generating a directed behavior graph according to the behavior data of the user and the behavior data of similar users of the user.
4. The user behavior analysis method according to claim 3, wherein determining whether the user is a directional demand user or a fuzzy demand user according to behavior data of the user specifically comprises:
and judging whether the user is a directional demand user or a fuzzy demand user according to the behavior similarity between a behavior data structure of the user and a user behavior logic model, and/or the resource similarity between the resource characteristics in the user behavior data and the resource roles in the pre-established resource role characteristic library, and/or the user similarity between the user characteristics of the user and the user roles in the pre-established user role characteristic library.
5. The user behavior analysis method according to claim 1 or 2, wherein calculating the behavior edge weight of the directed behavior graph and extracting the effective path specifically includes:
performing cluster analysis on the behavior data on a time scale, and calculating the time weight of the directed behavior graph to form a time behavior data graph;
performing clustering analysis on the behavior data on a spatial scale, and calculating the spatial weight of the directed behavior graph to form a spatial behavior data graph;
and calculating the behavior edge weight of the directed behavior graph according to the time weight and the space weight, and respectively extracting effective paths of the time behavior data graph and the space behavior data graph.
6. The user behavior analysis method according to claim 5, wherein the dividing of behavior data into directional requirements and fuzzy requirements specifically comprises:
combining the shortest paths of the time behavior data graph and the space behavior data graph to obtain the direct demand characteristics of the user;
analyzing the loop intersection points in the effective paths, extracting the loop-free paths, performing similarity analysis on the resource characteristics of the loop-free paths and the characteristics directly required by the user, and determining whether the behavior data corresponding to the loop-free paths is a directional requirement or a fuzzy requirement according to the similarity analysis result.
7. The user behavior analysis method according to claim 1, wherein generating the resource role framework according to the directed behavior graph specifically comprises:
extracting edges with weights higher than a preset value in the directed behavior graph as a sub-graph a;
traversing a user demand relation library, and screening a sub-graph b with the support degree higher than a support degree threshold;
setting the resource characteristic of the sub-graph a as A and the resource characteristic of the sub-graph B as B, and calculating confidence coefficients from A to B and from B to A;
and screening out the resource features with the confidence coefficient higher than the confidence coefficient threshold value, and forming a resource role framework by the screened-out resource features.
8. The user behavior analysis method according to claim 7, wherein generating a decision tree according to the resource role framework and the user requirement relationship library specifically comprises:
sequencing the resource characteristics of the resource role framework according to the confidence coefficient by adopting a bubbling method, and taking the obtained sequence as a main decision rule; inducing the user requirement relation library to form an auxiliary decision rule;
and generating a decision tree according to the main decision rule and the auxiliary decision rule.
9. A resource recommendation method, comprising:
acquiring fuzzy demand characteristics of a user;
the decision tree according to any one of claims 1 to 8, obtaining a demand feature with the highest relevance to the fuzzy demand feature and a resource role corresponding to the demand feature;
and recommending resources to the user according to the resource roles.
10. The resource recommendation method of claim 9, further comprising:
acquiring a user role according to the user characteristics of the user;
and recommending resources to the user according to the user role and the resource role.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811447333.3A CN109190051B (en) | 2018-11-29 | 2018-11-29 | User behavior analysis method and resource recommendation method based on analysis method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811447333.3A CN109190051B (en) | 2018-11-29 | 2018-11-29 | User behavior analysis method and resource recommendation method based on analysis method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109190051A CN109190051A (en) | 2019-01-11 |
| CN109190051B true CN109190051B (en) | 2020-11-03 |
Family
ID=64938398
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811447333.3A Active CN109190051B (en) | 2018-11-29 | 2018-11-29 | User behavior analysis method and resource recommendation method based on analysis method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109190051B (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110059315B (en) * | 2019-04-16 | 2023-07-07 | 广东省科技基础条件平台中心 | Scientific and technological resource perception fusion decision method |
| CN110097278B (en) * | 2019-04-28 | 2021-06-08 | 广东省科技基础条件平台中心 | Intelligent sharing and fusion training system and application system for scientific and technological resources |
| CN111400568B (en) * | 2020-03-13 | 2021-06-08 | 深圳市腾讯计算机***有限公司 | Behavior intention analysis method and device, electronic equipment and storage medium |
| CN113032694B (en) * | 2021-05-26 | 2021-11-09 | 浙江口碑网络技术有限公司 | Scene-based query method and device, storage medium and computer equipment |
| CN114861213B (en) * | 2022-07-07 | 2022-10-28 | 广东省科技基础条件平台中心 | Full-period intelligent management system and method for scientific and technological projects |
| US20240160727A1 (en) * | 2022-11-08 | 2024-05-16 | Akamai Technologies, Inc. | Detecting anomalous Application Programming Interface (API) behaviors |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102929892A (en) * | 2011-08-12 | 2013-02-13 | 莫润刚 | Accurate information promoting system and method based on social network |
| CN104063418A (en) * | 2014-03-17 | 2014-09-24 | 百度在线网络技术（北京）有限公司 | Search recommendation method and device |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140258175A1 (en) * | 2013-03-08 | 2014-09-11 | The Motley Fool Holdings, Inc. | Generating Personalized Investment Recommendations |
| CN104537095B (en) * | 2015-01-08 | 2018-02-02 | 吴锦锋 | A kind of accurate information method for pushing and system based on attraction model |
| CN106779146A (en) * | 2016-11-15 | 2017-05-31 | 广州铁路职业技术学院 | A kind of tourism service system for providing recommendation tourism route |
| CN108664649B (en) * | 2018-05-17 | 2021-09-24 | 邓北平 | Knowledge content pushing method and device and pushing server |
-
2018
- 2018-11-29 CN CN201811447333.3A patent/CN109190051B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102929892A (en) * | 2011-08-12 | 2013-02-13 | 莫润刚 | Accurate information promoting system and method based on social network |
| CN104063418A (en) * | 2014-03-17 | 2014-09-24 | 百度在线网络技术（北京）有限公司 | Search recommendation method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109190051A (en) | 2019-01-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109190051B (en) | User behavior analysis method and resource recommendation method based on analysis method | |
| CN106649260B (en) | Product characteristic structure tree construction method based on comment text mining | |
| CN105335496B (en) | Customer service based on cosine similarity text mining algorithm repeats call processing method | |
| CN103544255B (en) | Text semantic relativity based network public opinion information analysis method | |
| CN110825877A (en) | Semantic similarity analysis method based on text clustering | |
| CN110633330B (en) | Event discovery method, device, equipment and storage medium | |
| CN104077407B (en) | A kind of intelligent data search system and method | |
| US20110231399A1 (en) | Clustering Method and System | |
| CN108304382B (en) | Quality analysis method and system based on text data mining in manufacturing process | |
| CN114549874A (en) | Training method of multi-target image-text matching model, image-text retrieval method and device | |
| CN105389341A (en) | Text clustering and analysis method for repeating caller work orders of customer service calls | |
| CN112559747A (en) | Event classification processing method and device, electronic equipment and storage medium | |
| CN103218368B (en) | A kind of method and apparatus excavating hot word | |
| CN114970553B (en) | Information analysis method and device based on large-scale unmarked corpus and electronic equipment | |
| CN108647729A (en) | A kind of user's portrait acquisition methods | |
| CN116049379A (en) | Knowledge recommendation method, knowledge recommendation device, electronic equipment and storage medium | |
| Song et al. | Interactive visual pattern search on graph data via graph representation learning | |
| CN116756373A (en) | Project review expert screening method, system and medium based on knowledge graph update | |
| Shen et al. | A cross-database comparison to discover potential product opportunities using text mining and cosine similarity | |
| CN114491232B (en) | Information query method and device, electronic equipment and storage medium | |
| CN115757735A (en) | Intelligent retrieval method and system for power grid digital construction result resources | |
| CN115409541A (en) | Cigarette brand data processing method based on data blood relationship | |
| CN113342844A (en) | Industrial intelligent search system | |
| CN114185875A (en) | Big data unified analysis and processing system based on cloud computing | |
| CN112949304A (en) | Construction case knowledge reuse query method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB03 | Change of inventor or designer information | ||
| CB03 | Change of inventor or designer information |
Inventor after: Zhou Junjie Inventor after: Zhao Xiaomeng Inventor after: Li Sha Inventor after: Fang Shaoliang Inventor after: Lin Zhu Inventor after: Luo Liang Inventor before: Zhou Junjie Inventor before: Li Sha Inventor before: Zhao Xiaomeng Inventor before: Fang Shaoliang Inventor before: Lin Zhu Inventor before: Luo Liang |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |