CN109543940B - Activity evaluation method, activity evaluation device, electronic equipment and storage medium - Google Patents
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Abstract
The embodiment of the invention provides an activity evaluation method, an activity evaluation device, electronic equipment and a storage medium, and relates to the technical field of big data. The method comprises the following steps: acquiring activity information of a plurality of operation activities, and carrying out clustering processing on the plurality of operation activities based on the activity information to obtain a plurality of class clusters; acquiring historical behavior data of users under each operation activity in each type of cluster; determining the daily user activity and daily participation number of each operation activity in each type of cluster based on the historical behavior data; and evaluating each operation activity in each type of cluster based on the daily user activity and the daily participation number. The technical scheme of the embodiment of the invention can carry out classified evaluation on different types of operation activities, and evaluate the operation activities by combining the number of users involved and the two dimensions of liveness, thereby improving the accuracy of evaluation results.
Description
Technical Field
The present invention relates to the field of big data technology, and in particular, to an activity assessment method, an activity assessment device, an electronic apparatus, and a computer-readable storage medium.
Background
With the development of internet technology, more and more enterprises choose to perform online activities on the internet, and quantitative evaluation of online operation activities is required.
Currently, in one technical solution, an active operator counts the number of participants each day of each operation activity, and evaluates the operation activity based on the counted result. In the technical scheme, because the attraction degree of different types of activities to users is different, the operation effect of the activities is difficult to accurately evaluate only by the number of participants.
Accordingly, it is desirable to provide an activity assessment method, an activity assessment apparatus, an electronic device, and a computer-readable storage medium capable of solving one or more of the problems described above.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the invention and thus may include information that does not form the prior art that is already known to those of ordinary skill in the art.
Disclosure of Invention
It is an object of embodiments of the present invention to provide an activity assessment method, an activity assessment apparatus, an electronic device, and a computer-readable storage medium, which overcome, at least in part, one or more of the problems due to the limitations and disadvantages of the related art.
According to a first aspect of an embodiment of the present invention, there is provided an activity assessment method, including: acquiring activity information of a plurality of operation activities, and carrying out clustering processing on the operation activities based on the activity information to obtain a plurality of class clusters; acquiring historical behavior data of users under each operation activity in each type of cluster; determining the daily user activity and daily participation number of each operation activity in each type of cluster based on the historical behavior data; and evaluating each operation activity in each type of cluster based on the daily user activity and the daily participation number.
In some embodiments of the present invention, based on the foregoing solution, determining, based on the historical behavior data, daily average user activity of each operation activity in each class of clusters includes: counting historical behavior data of users under each operation activity in each type of cluster, wherein the historical behavior data comprises login times, clicking times, accumulated access time, comment times and coupon use times; weighting the login times, the clicking times, the accumulated access time, the comment times and the coupon use times in the historical behavior data to determine the user activity of each operation activity; and determining the daily user activity of each operation activity in each type of cluster based on the user activity of each operation activity and the duration days of the activity.
In some embodiments of the present invention, based on the foregoing solution, the evaluating each operation activity in each type of cluster based on the average daily user activity and the average daily participation count includes: counting the number of active users with the user activity degree of each operation activity being greater than a first preset threshold value; determining the daily active user number of each operation activity based on the number of active users of each operation activity and the duration days of the activity; and evaluating each operation activity in each type of cluster based on the daily active user number and the daily participation number.
In some embodiments of the present invention, based on the foregoing solution, the evaluating each operation activity in each type of cluster based on the number of average daily active users and the number of average daily participants includes: determining the proportion of the number of active users of each operation activity to the total number of the users; determining the weight of the average number of active users and the average number of participants based on the proportion; weighting the average active user number and the average participation number based on the weight; and evaluating each operation activity in each type of cluster based on the result of the weighting operation.
In some embodiments of the present invention, based on the foregoing scheme, determining a daily participation number of each operation activity in each class of clusters based on the historical behavior data includes: determining the daily participation number of each operation activity in each type of cluster based on the historical behavior data by the following formula,
wherein Y is the number of people participating in the operation activity on average, X is the duration of the activity, a is the parameter related to the total number of people, and b is the parameter related to the duration of the activity.
In some embodiments of the invention, based on the foregoing, the activity assessment method further comprises: and sequencing the operation activities in each type of cluster based on the result of the evaluation and the online time of the operation activities.
In some embodiments of the present invention, based on the foregoing solution, sorting the operation activities in each class of clusters based on the result of the evaluation and the online time of the operation activities includes: ranking the operational activities in each class of clusters in descending order based on the assessed scores; determining time weights of the operation activities based on online time of the operation activities; multiplying the time weight of each operational activity by the estimated score, and adjusting the order of the descending order based on the result of the multiplication.
According to a second aspect of an embodiment of the present invention, there is provided an activity assessment apparatus including: the clustering unit is used for acquiring activity information of a plurality of operation activities, and carrying out clustering processing on the operation activities based on the activity information to acquire a plurality of class clusters; the data acquisition unit is used for acquiring historical behavior data of the user under each operation activity in each type of cluster; the determining unit is used for determining the daily user activity and the daily participation number of each operation activity in each type of cluster based on the historical behavior data; and the evaluation unit is used for evaluating each operation activity based on the daily user activity level and the daily participation number of each operation activity under each type of cluster.
According to a third aspect of an embodiment of the present invention, there is provided an electronic apparatus including: a processor; and a memory having stored thereon a computer program which, when executed by the processor, implements the activity assessment method as described in the first aspect above.
According to a fourth aspect of embodiments of the present invention, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the activity assessment method as described in the first aspect above.
In the technical schemes provided by some embodiments of the present invention, on one hand, the operation activities are clustered based on the activity information, so that different types of operation activities can be classified and evaluated, and the evaluation result is more reasonable and accurate; on the other hand, the operation activities are evaluated according to the daily user liveness and the daily participation number in the activity period, the operation activities can be evaluated by combining the number of the participation users and the two dimensions of the liveness, the accuracy of the evaluation result is further improved, and the operation resources can be conveniently optimally configured according to the evaluation result.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.
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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. It is evident that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art. In the drawings:
FIG. 1 illustrates a flow diagram of a method of activity assessment according to some embodiments of the invention;
FIG. 2 illustrates a flow diagram for evaluating various operational activities based on average daily user activity and average daily participant numbers in accordance with some embodiments of the invention;
FIG. 3 illustrates a flow diagram for ordering operational activities in a class cluster based on the results of the evaluation and the time of the online of each operational activity, according to some embodiments of the invention;
FIG. 4 illustrates a schematic block diagram of an activity assessment device according to some embodiments of the invention;
fig. 5 shows a schematic diagram of a computer system suitable for use in implementing an embodiment of the invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be embodied in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the invention.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
FIG. 1 illustrates a flow diagram of a method of activity assessment according to some embodiments of the invention.
Referring to fig. 1, in step S110, activity information of a plurality of operation activities is acquired, and a plurality of class clusters are obtained by performing a clustering process on the plurality of operation activities based on the activity information.
In an example embodiment, the plurality of operational activities may include: insurance type activities, financial type operations activities, fund type operations activities, health type operations activities, and life type operations activities. The activity information of the operation activity may include: information such as an activity name, activity content, preferential mode and participation mode of an operation activity.
Further, the activity content information of the plurality of operation activities may be subjected to word segmentation processing to obtain word vectors of the content of each operation activity, and each operation activity may be clustered based on the word vectors of the content of each operation activity. For example, word2vec may be used to perform word segmentation on the activity content information of multiple operation activities, to obtain word vectors of the activity content of the operation activities, and cluster each operation activity based on distances between word vectors of the content of each operation activity and based on distances between word vectors of each operation activity. By carrying out clustering processing on a plurality of operation activities, classification evaluation can be carried out on different types of operation activities, so that the accuracy of evaluation can be improved.
In an example embodiment, the clustering operations may include K-means clustering operations or K-neutral clustering operations, but may also be other clustering operations such as hierarchical clustering operations or density-based clustering operations.
It should be noted that, the distance between word vectors may be a hamming distance, a euclidean distance, a cosine distance, but the distance in the exemplary embodiment of the present invention is not limited thereto, and may be a mahalanobis distance, a manhattan distance, or the like, for example.
In step S120, historical behavior data of the user under each operation activity in each cluster is obtained.
In an example embodiment, the historical behavior data of the user may include: the daily user click quantity, daily activity participation number, daily new user number, coupon acquisition use quantity, daily accumulated access duration of each user and other data in the whole operation activity period.
For example, for a class cluster of insurance class activities, a participation activity record, such as browsing time, browsing times and coupon acquisition use record, of a user corresponding to each operation activity in the class cluster can be obtained from a database based on the name and activity time of the operation activity, and historical behavior data, such as the user click amount, the number of activity participants, coupon acquisition use condition, accumulated access duration, and the like, of the operation activity can be determined based on the participation activity record of the user.
In step S130, the daily user activity level and the daily participation number of each operation activity in each type of cluster are determined based on the historical behavior data.
In an example embodiment, the calculation may be based on the number of logins of the user, the number of clicks of the user, the accumulated access time length of the user, the number of comments of the user, and the number of coupon uses. For example, the activity of the user is obtained by weighting the login times of the user, the clicking times of the user, the accumulated access time of the user, the comment times of the user and the coupon use number, and the activity of the user is divided by the activity duration days to obtain the daily average activity of the activity.
In an example embodiment, the daily participation number of each operation activity in each type of cluster is determined by the following formula (1) based on the historical behavior data,
wherein Y is the number of people participating in the operation activity on average, X is the duration of the activity, a is the parameter related to the total number of people, and b is the parameter related to the duration of the activity.
In step S140, each operation activity in each type of cluster is evaluated based on the daily user activity level and the daily participation count.
The daily average participation number of the operation activities can reflect the capability of the activity obtaining users, the daily average user activity of the activities can reflect the capability of the activity retaining users, the number of the participation users of the operation activities is large, and the activity of the participation users is high. In an example embodiment, a weighting operation may be performed on the daily user activity of the operation activity and the daily number of people involved in the operation activity, and each operation activity in each type of cluster may be evaluated according to the result of the weighting operation. For example, the average user activity may be weighted 20% and the average number of people involved in the operation may be weighted 80%, and the average user activity and average number of people involved in the operation may be weighted based on the set weight.
Further, in some example embodiments, the number of active users for each operational activity may also be determined as a proportion of the total number of users; determining the daily user activity and the weight of the daily participation number based on the proportion; weighting the average user activity and the average number of people participating in the day based on the weight; and evaluating each operation activity in each type of cluster based on the result of the weighting operation.
According to the activity evaluation method shown in fig. 1, on one hand, operation activities are clustered based on activity information, and different types of operation activities can be classified and evaluated, so that evaluation results are more reasonable and accurate; on the other hand, the operation activities are evaluated according to the daily user liveness and the daily participation number in the activity period, the operation activities can be evaluated by combining the number of the participation users and the two dimensions of the liveness, the accuracy of the evaluation result is further improved, and the operation resources can be conveniently optimally configured according to the evaluation result.
Further, in an example embodiment, the operations in each class of cluster may also be ordered based on the results of the evaluation and the time of the online of the operations. For example, the operational activities in each class of clusters may be ranked based on the results of the evaluation, and the ranked results may be adjusted based on the time of the operational activities' online. The operation strategies can be adjusted according to the evaluation results to sort the operation activities so as to improve the user activity degree of the activities and attract more users to participate in the activities, and the operation labor cost, the flow cost and the financial cost can be conveniently optimized according to the evaluation results.
In addition, in order to protect the new online activities, for the new operation activities with online days less than 3 days, the new operation activities are fixedly placed at the 2 nd, 4 th, 6 th and 8 th positions of the ordered list according to the online time of the operation activities, so that the new operation activities have enough time verification effect.
Fig. 2 illustrates a flow diagram for evaluating various operational activities based on average user activity and average number of people engaged in the day, according to some embodiments of the invention.
Referring to fig. 2, in step S210, the number of active users whose user liveness for each operation activity is greater than a first predetermined threshold is counted.
In an example embodiment, the number of logins, the number of clicks, the accumulated access duration, the number of comments, and the number of coupon uses of the user may be weighted to determine the user activity of each operation activity. And determining the users with the user liveness of each operation activity being greater than a first preset threshold value as active users, and counting the number of the active users of each operation activity. The first predetermined threshold is a threshold determined according to the total number of the operation activities and the range of the values of the user liveness, and may be an liveness average value of historical data of the user liveness in the past year, which is not particularly limited in the present invention.
In step S220, the daily active number of users for each operation activity is determined based on the number of active users for each operation activity and the activity duration days.
In an example embodiment, dividing the number of active users for each operational activity by the activity duration may determine the average number of active users for each operational activity. Based on the daily active user number of each operation activity, the operation activity is evaluated, and the active user can reflect the operation effect of the activity, so that the evaluation accuracy can be further improved.
In step S230, each operation activity in each cluster is evaluated based on the number of daily active users and the number of daily participants.
In an example embodiment, a weighting operation may be performed on the number of daily active users and the number of daily participants for each operation activity, and each operation activity may be evaluated based on the result of the weighting operation. For example, the weight of the average number of users on the day may be set to 50%, the weight of the average number of people on the day may be set to 50%, and the average user activity and average number of people on the day for the operation activity may be weighted based on the set weight.
Fig. 3 illustrates a flow diagram for ordering operational activities in a class cluster based on the results of the evaluation and the time of the online of each operational activity, according to some embodiments of the invention.
Referring to fig. 3, in step S310, operation activities in each class of clusters are arranged in descending order based on the evaluated scores.
In an example embodiment, the operational activities in each class of clusters may be ranked in descending order based on the evaluation scores of the operational activities in each class of clusters. The evaluation score may be an evaluation score based on the average user activity and average number of people involved in the day, or an evaluation score based on the average number of users involved in the day and average number of people involved in the day.
In step S320, a time weight for each operation activity is determined based on the online time of each operation activity.
In an example embodiment, when the online time of the operation activity is short, in order to protect the operation activity that is online soon, the time weight of the operation activity may be set higher, and when the online time of the operation activity is long, the time weight of the operation activity may be set lower.
In step S330, the time weight of each operation activity is multiplied by the score of the evaluation, and the order of the descending order is adjusted based on the result of the multiplication.
In an example embodiment, after determining the time weights of the operation activities in the class cluster, the time weights of the operation activities are different from the evaluation scores of the operation activities, and the operation activities are reordered based on the multiplied results.
In addition, in an example embodiment of the present invention, an activity evaluation apparatus is also provided. Referring to fig. 4, the activity assessment apparatus 400 may include: a clustering unit 410, a data acquisition unit 420, a determination unit 430 and an evaluation unit 440. The clustering unit 410 is configured to obtain activity information of a plurality of operation activities, and perform clustering processing on the plurality of operation activities based on the activity information to obtain a plurality of class clusters; the data obtaining unit 420 is configured to obtain historical behavior data of the user under each operation activity in each cluster; the determining unit 430 is configured to determine, based on the historical behavior data, a daily average user activity level and a daily average number of participants of each operation activity in each cluster; the evaluation unit 440 is configured to evaluate each operation activity based on the daily user activity level and the daily participation number of each operation activity under each type of cluster.
In some embodiments of the present invention, based on the foregoing scheme, the determining unit 430 includes: the first statistics unit is used for counting historical behavior data of users under each operation activity in each type of cluster, wherein the historical behavior data comprise login times, click times, accumulated access time, comment times and coupon use times; the user activity determining unit is used for carrying out weighted operation on the login times, the clicking times, the accumulated access time length, the comment times and the coupon use times in the historical behavior data to determine the user activity of each operation activity; and the daily user activity determination unit is used for determining the daily user activity of each operation activity in each type of cluster based on the user activity of each operation activity and the duration days of the activity.
In some embodiments of the present invention, based on the foregoing scheme, the evaluation unit 440 includes: the second statistics unit is used for counting the number of active users with the user activity degree of each operation activity being greater than a first preset threshold value; a daily active user number determining unit, configured to determine a daily active user number of each operation activity based on the number of active users of each operation activity and the duration days of the activity; the first evaluation unit is used for evaluating each operation activity in each type of cluster based on the daily active user number and the daily participation number.
In some embodiments of the invention, based on the foregoing, the first evaluation unit is configured to: determining the proportion of the number of active users of each operation activity to the total number of the users; determining the weight of the average number of active users and the average number of participants based on the proportion; weighting the average active user number and the average participation number based on the weight; and evaluating each operation activity in each type of cluster based on the result of the weighting operation.
In some embodiments of the invention, based on the foregoing scheme, the determining unit is configured to: determining the daily participation number of each operation activity in each type of cluster based on the historical behavior data by the following formula,
wherein Y is the number of people participating in the operation activity on average, X is the duration of the activity, a is the parameter related to the total number of people, and b is the parameter related to the duration of the activity.
In some embodiments of the present invention, based on the foregoing scheme, the activity assessment apparatus 400 further includes: and the sequencing unit is used for sequencing the operation activities in each type of cluster based on the result of the evaluation and the online time of the operation activities.
In some embodiments of the invention, based on the foregoing scheme, the sorting unit is configured to: ranking the operational activities in each class of clusters in descending order based on the assessed scores; determining time weights of the operation activities based on online time of the operation activities; multiplying the time weight of each operational activity by the estimated score, and adjusting the order of the descending order based on the result of the multiplication.
Since the respective functional modules of the activity assessment apparatus 400 of the exemplary embodiment of the present invention correspond to the steps of the exemplary embodiment of the activity assessment method described above, a detailed description thereof will be omitted.
In an exemplary embodiment of the present invention, an electronic device capable of implementing the above method is also provided.
Referring now to FIG. 5, there is illustrated a schematic diagram of a computer system 500 suitable for use in implementing an electronic device of an embodiment of the present invention. The computer system 500 of the electronic device shown in fig. 5 is only an example and should not be construed as limiting the functionality and scope of use of embodiments of the invention.
As shown in fig. 5, the computer system 500 includes a Central Processing Unit (CPU) 501, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the system operation are also stored. The CPU 501, ROM 502, and RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following components are connected to the I/O interface 505: an input section 506 including a keyboard, a mouse, and the like; an output portion 507 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker, and the like; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The drive 510 is also connected to the I/O interface 505 as needed. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as needed so that a computer program read therefrom is mounted into the storage section 508 as needed.
In particular, according to embodiments of the present invention, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 509, and/or installed from the removable media 511. The above-described functions defined in the system of the present application are performed when the computer program is executed by a Central Processing Unit (CPU) 501.
The computer readable medium shown in the present invention may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present invention may be implemented by software, or may be implemented by hardware, and the described units may also be provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
As another aspect, the present application also provides a computer-readable medium that may be contained in the electronic device described in the above embodiment; or may exist alone without being incorporated into the electronic device. The computer-readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to implement the activity assessment method as described in the above embodiments.
For example, the electronic device may implement the method as shown in fig. 1: step S110, acquiring activity information of a plurality of operation activities, and carrying out clustering processing on the operation activities based on the activity information to obtain a plurality of class clusters; step S120, acquiring historical behavior data of users under each operation activity in each type of cluster; step S130, determining the daily user activity level and the daily participation number of each operation activity in each type of cluster based on the historical behavior data; and step S140, evaluating each operation activity in each type of cluster based on the daily user activity and the daily participation number.
It should be noted that although in the above detailed description several modules or units of a device or means for action execution are mentioned, such a division is not mandatory. Indeed, the features and functions of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the invention. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present invention may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a touch terminal, or a network device, etc.) to perform the method according to the embodiments of the present invention.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It is to be understood that the invention is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (5)
1. A method of activity assessment, comprising:
acquiring activity information of a plurality of operation activities, clustering the operation activities based on the activity information to obtain a plurality of class clusters, wherein the method comprises the following steps: performing word segmentation processing on the activity content information of the plurality of operation activities to obtain word vectors of the content of each operation activity, and clustering each operation activity based on the distance between the word vectors of the content of each operation activity;
acquiring historical behavior data of users under each operation activity in each type of cluster;
determining the daily user activity level and the daily participation number of each operation activity in each type of cluster based on the historical behavior data, wherein the method comprises the following steps: counting historical behavior data of users under each operation activity in each type of cluster, wherein the historical behavior data comprises login times, clicking times, accumulated access time, comment times and coupon use times; weighting the login times, the clicking times, the accumulated access time, the comment times and the coupon use times in the historical behavior data to determine the user activity of each operation activity; determining the daily average user activity of each operation activity in each type of cluster based on the user activity of each operation activity and the duration days of the activity;
evaluating each operation activity in each type of cluster based on the average daily user activity and the average daily participation number, including: counting the number of active users with the user activity degree of each operation activity being greater than a first preset threshold value; determining the daily active user number of each operation activity based on the number of active users of each operation activity and the duration days of the activity; determining the proportion of the number of active users of each operation activity to the total number of the users; determining the weight of the average number of active users and the average number of participants based on the proportion; weighting the average active user number and the average participation number based on the weight; evaluating each operation activity in each type of cluster based on the result of the weighting operation;
ranking the operational activities in each class of clusters in descending order based on the assessed scores;
determining time weights of the operation activities based on online time of the operation activities;
multiplying the time weights of the operational activities with the estimated scores, and adjusting the descending order of the orders based on the multiplied results;
the method for determining the daily participation number of each operation activity in each type of cluster based on the historical behavior data comprises the following steps: determining the daily participation number of each operation activity in each type of cluster based on the historical behavior data by the following formula,
wherein Y is the number of people participating in the operation activity on average, X is the duration of the activity, a is the parameter related to the total number of people, and b is the parameter related to the duration of the activity.
2. The activity assessment method according to claim 1, wherein the activity assessment method further comprises:
and sequencing the operation activities in each type of cluster based on the result of the evaluation and the online time of the operation activities.
3. An activity assessment device, comprising:
the clustering unit is used for acquiring activity information of a plurality of operation activities, clustering the operation activities based on the activity information to acquire a plurality of class clusters, and comprises the following steps: performing word segmentation processing on the activity content information of the plurality of operation activities to obtain word vectors of the content of each operation activity, and clustering each operation activity based on the distance between the word vectors of the content of each operation activity;
the data acquisition unit is used for acquiring historical behavior data of the user under each operation activity in each type of cluster;
the determining unit is configured to determine, based on the historical behavior data, daily average user activity and daily average participation number of each operation activity in each type of cluster, and includes: counting historical behavior data of users under each operation activity in each type of cluster, wherein the historical behavior data comprises login times, clicking times, accumulated access time, comment times and coupon use times; weighting the login times, the clicking times, the accumulated access time, the comment times and the coupon use times in the historical behavior data to determine the user activity of each operation activity; determining the daily average user activity of each operation activity in each type of cluster based on the user activity of each operation activity and the duration days of the activity;
the evaluation unit is used for evaluating each operation activity based on the daily user activity level and the daily participation number of each operation activity under each type of cluster, and comprises the following steps: counting the number of active users with the user activity degree of each operation activity being greater than a first preset threshold value; determining the daily active user number of each operation activity based on the number of active users of each operation activity and the duration days of the activity; determining the proportion of the number of active users of each operation activity to the total number of the users; determining the weight of the average number of active users and the average number of participants based on the proportion; weighting the average active user number and the average participation number based on the weight; evaluating each operation activity in each type of cluster based on the result of the weighting operation;
a ranking unit for ranking the operation activities in each class of clusters in descending order based on the evaluated scores; determining time weights of the operation activities based on online time of the operation activities; multiplying the time weights of the operational activities with the estimated scores, and adjusting the descending order of the orders based on the multiplied results;
the method for determining the daily participation number of each operation activity in each type of cluster based on the historical behavior data comprises the following steps: determining the daily participation number of each operation activity in each type of cluster based on the historical behavior data by the following formula,
wherein Y is the number of people participating in the operation activity on average, X is the duration of the activity, a is the parameter related to the total number of people, and b is the parameter related to the duration of the activity.
4. An electronic device, comprising:
a processor; and
a memory having stored thereon a computer program which, when executed by the processor, implements the activity assessment method according to any one of claims 1 or 2.
5. A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the activity assessment method according to any one of claims 1 or 2.
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|---|---|---|---|---|
| CN110188977B (en) * | 2019-04-10 | 2023-06-02 | 创新先进技术有限公司 | Project audit member scheduling method and device |
| CN116720829A (en) * | 2019-04-10 | 2023-09-08 | 创新先进技术有限公司 | Method and device for evaluating audit group members |
| CN110148012A (en) * | 2019-04-15 | 2019-08-20 | 中国平安人寿保险股份有限公司 | User's motivational techniques, device, computer equipment and storage medium |
| CN110796474A (en) * | 2019-09-18 | 2020-02-14 | 北京你财富计算机科技有限公司 | Automatic adjustment method and device for activity operation and electronic equipment |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005348926A (en) * | 2004-06-10 | 2005-12-22 | Mars Engineering Corp | Information processor, information processing method and information processing system |
| CN102193936A (en) * | 2010-03-09 | 2011-09-21 | 阿里巴巴集团控股有限公司 | Data classification method and device |
| CN105677677A (en) * | 2014-11-20 | 2016-06-15 | 阿里巴巴集团控股有限公司 | Information classification and device |
| CN106600007A (en) * | 2016-11-29 | 2017-04-26 | 上海亿账通互联网科技有限公司 | Activity quality evaluation method and device |
| CN106789136A (en) * | 2015-11-19 | 2017-05-31 | 北大方正集团有限公司 | OA operation analysis implementation method and device based on user behavior |
| CN106934646A (en) * | 2015-12-31 | 2017-07-07 | 阿里巴巴集团控股有限公司 | The method and apparatus that electronic coupons are delivered in marketing activity |
| CN107370614A (en) * | 2016-05-13 | 2017-11-21 | 北京京东尚科信息技术有限公司 | Network user active degree appraisal procedure and Forecasting Methodology |
| WO2018028099A1 (en) * | 2016-08-09 | 2018-02-15 | 百度在线网络技术（北京）有限公司 | Method and device for search quality assessment |
| CN108259638A (en) * | 2017-12-11 | 2018-07-06 | 海南智媒云图科技股份有限公司 | Personal group list intelligent sorting method, intelligent terminal and storage medium |
| CN108400929A (en) * | 2018-02-08 | 2018-08-14 | 网易乐得科技有限公司 | Data processing method, device, computing device and medium |
-
2018
- 2018-10-12 CN CN201811191497.4A patent/CN109543940B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005348926A (en) * | 2004-06-10 | 2005-12-22 | Mars Engineering Corp | Information processor, information processing method and information processing system |
| CN102193936A (en) * | 2010-03-09 | 2011-09-21 | 阿里巴巴集团控股有限公司 | Data classification method and device |
| CN105677677A (en) * | 2014-11-20 | 2016-06-15 | 阿里巴巴集团控股有限公司 | Information classification and device |
| CN106789136A (en) * | 2015-11-19 | 2017-05-31 | 北大方正集团有限公司 | OA operation analysis implementation method and device based on user behavior |
| CN106934646A (en) * | 2015-12-31 | 2017-07-07 | 阿里巴巴集团控股有限公司 | The method and apparatus that electronic coupons are delivered in marketing activity |
| CN107370614A (en) * | 2016-05-13 | 2017-11-21 | 北京京东尚科信息技术有限公司 | Network user active degree appraisal procedure and Forecasting Methodology |
| WO2018028099A1 (en) * | 2016-08-09 | 2018-02-15 | 百度在线网络技术（北京）有限公司 | Method and device for search quality assessment |
| CN106600007A (en) * | 2016-11-29 | 2017-04-26 | 上海亿账通互联网科技有限公司 | Activity quality evaluation method and device |
| CN108259638A (en) * | 2017-12-11 | 2018-07-06 | 海南智媒云图科技股份有限公司 | Personal group list intelligent sorting method, intelligent terminal and storage medium |
| CN108400929A (en) * | 2018-02-08 | 2018-08-14 | 网易乐得科技有限公司 | Data processing method, device, computing device and medium |
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|---|---|
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