CN107357847B - Data processing method and device - Google Patents
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- CN107357847B CN107357847B CN201710498852.1A CN201710498852A CN107357847B CN 107357847 B CN107357847 B CN 107357847B CN 201710498852 A CN201710498852 A CN 201710498852A CN 107357847 B CN107357847 B CN 107357847B
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- G06F16/90—Details of database functions independent of the retrieved data types
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Abstract
The present disclosure provides a data processing method, including: acquiring first actual operation data of a target user on a specified category; acquiring expected operation data of a target user on an appointed category, wherein the expected operation data is used as reference data for measuring interest deviation of the target user on the appointed category, the expected operation data is related to actual operation data generated when a plurality of users operate the plurality of categories, and the appointed category belongs to one of the plurality of categories; and determining the interest deviation of the target user to the specified category according to the first actual operation data and the expected operation data. The present disclosure also provides a data processing apparatus, a computer-readable storage medium, and a data processing system.
Description
Technical Field
The present disclosure relates to the field of data processing, and more particularly, to a data processing method and apparatus.
Background
With the rapid development of artificial intelligence, automation, and computer technology, the impact of data processing and analysis capabilities on e-commerce is becoming increasingly important. For example, in the e-commerce field, in the face of massive commodity data and user data, the accuracy of data analysis has a very important influence on the marketing of merchants and the decision of customer transaction.
In the process of implementing the inventive concept, the inventor finds that at least the following problems exist in the prior art: in the face of massive commodity classification (product class for short) data and user data, the effectiveness of the data is measured by using univariate statistics in the prior art, so that the data analysis accuracy is low.
Disclosure of Invention
In view of the above, the present disclosure provides a data processing method, an apparatus and a system thereof, and a computer-readable storage medium capable of improving data analysis accuracy.
One aspect of the present disclosure provides a data processing method, including: acquiring first actual operation data of a target user on a specified category; acquiring expected operation data of the target user on the specified category, wherein the expected operation data is used as reference data for measuring interest deviation of the target user on the specified category, and the expected operation data is related to actual operation data generated when a plurality of users operate a plurality of categories, and the specified category belongs to one of the plurality of categories; and determining the interest deviation of the target user to the specified category according to the first actual operation data and the expected operation data.
According to an embodiment of the present disclosure, the obtaining of the expected operation data of the target user on the specified category includes: determining the probability of occurrence of an event, wherein the event is the operation of the plurality of users on the specified category; acquiring second actual operation data, wherein the second actual operation data is the sum of actual operation data generated when the target user operates the multiple categories; and determining the expected operation data according to the second actual operation data and the probability of the event.
According to an embodiment of the present disclosure, determining the probability of an event occurrence comprises: acquiring third actual operation data, wherein the third actual operation data comprises the sum of actual operation data generated when the plurality of users operate the plurality of categories; acquiring fourth actual operation data, wherein the fourth actual operation data comprises the sum of actual operation data generated when the plurality of users operate the specified category; and determining the ratio of the fourth actual operation data to the third actual operation data to obtain the probability of the occurrence of the event.
According to an embodiment of the present disclosure, determining the interest deviation of the target user for the designated item according to the first actual operation data and the expected operation data includes: judging whether the numerical value corresponding to the first actual operation data is larger than the numerical value corresponding to the expected operation data; and if so, determining that the target user is interested in the specified category.
According to an embodiment of the present disclosure, determining the interest deviation of the target user for the designated item according to the first actual operation data and the expected operation data includes: calculating interest deviation values of the target users to the specified categories according to the first actual operation data and the expected operation data; and determining the interest deviation of the target user to the specified category according to the interest deviation value.
According to an embodiment of the present disclosure, calculating the interest deviation value of the target user for the designated item according to the first actual operation data and the expected operation data includes: determining an adjustment factor according to the first actual operation data and the expected operation data, wherein the adjustment factor is used for adjusting the interest deviation value; and calculating the interest deviation value of the target user to the specified item according to the first actual operation data, the expected operation data and the adjusting coefficient.
According to an embodiment of the present disclosure, after determining the interest deviation of the target user in the designated item according to the first actual operation data and the expected operation data, the method further includes: judging whether the determined interest deviation of the target user to the specified categories meets expected indexes or not; and if not, filtering the first actual operation data.
Another aspect of the present disclosure provides a data processing apparatus including: the first acquisition module is used for acquiring first actual operation data of a target user on a specified category; a second obtaining module, configured to obtain expected operation data of the target user on the specified category, where the expected operation data is used as reference data for measuring a deviation of interest of the target user on the specified category, and the expected operation data is related to actual operation data generated when multiple users operate multiple categories, and the specified category belongs to one of the multiple categories; and a first determining module, configured to determine, according to the first actual operation data and the expected operation data, a deviation of interest of the target user in the designated category.
According to an embodiment of the present disclosure, the second obtaining module includes: a first determining unit, configured to determine a probability of occurrence of an event, where the event is an operation performed on the designated item by the plurality of users; an obtaining unit, configured to obtain second actual operation data, where the second actual operation data is a sum of actual operation data generated when the target user operates the multiple categories; and a second determining unit configured to determine the expected operation data according to the second actual operation data and the probability of the event occurrence.
According to an embodiment of the present disclosure, the first determining unit includes: a first obtaining subunit, configured to obtain third actual operation data, where the third actual operation data includes a sum of actual operation data generated when the multiple users operate the multiple categories; a second obtaining subunit, configured to obtain fourth actual operation data, where the fourth actual operation data includes a sum of actual operation data generated when the plurality of users operate the specified category; and the first determining subunit is used for determining the ratio of the fourth actual operation data to the third actual operation data to obtain the probability of the occurrence of the event.
According to an embodiment of the present disclosure, the first determining module includes: a judging unit, configured to judge whether a numerical value corresponding to the first actual operation data is greater than a numerical value corresponding to the expected operation data; and a second determination unit, configured to determine that the target user is interested in the designated item when the value corresponding to the first actual operation data is greater than the value corresponding to the expected operation data.
According to an embodiment of the present disclosure, the first determining module includes: a calculating unit, configured to calculate an interest deviation value of the target user for the designated item according to the first actual operation data and the expected operation data; and a third determining unit, configured to determine, according to the interest deviation value, an interest deviation of the target user with respect to the designated item.
According to an embodiment of the present disclosure, the above calculation unit includes: a second determining subunit, configured to determine an adjustment coefficient according to the first actual operation data and the desired operation data, where the adjustment coefficient is used to adjust the interest deviation value; and a calculating subunit, configured to calculate, according to the first actual operation data, the expected operation data, and the adjustment coefficient, an interest deviation value of the target user for the designated item.
According to an embodiment of the present disclosure, the apparatus further includes: a judging module, configured to determine, according to the first actual operation data and the expected operation data, an interest deviation of the target user with respect to the specified category, and then judge whether the determined interest deviation of the target user with respect to the specified category meets an expected index; and the filtering module is used for filtering the first actual operation data under the condition that the judged interest deviation of the target user to the specified category accords with an expected index.
Another aspect of the present disclosure provides a computer-readable storage medium having stored thereon executable instructions, which when executed by a processor, are used to implement the above-mentioned data processing method.
Another aspect of the present disclosure provides a data processing system comprising: the computer-readable storage medium described above; and the processor.
Another aspect of the disclosure provides a computer program comprising computer executable instructions for implementing the method as described above when executed.
According to the embodiment of the disclosure, since the actual operation data of the user on the specified category is compared with the expected operation data, wherein the expected operation data is related to the operation data of the plurality of users on the plurality of categories, in this case, the expected operation data can change along with the change of the operation data of the plurality of users on different categories, that is, the expected operation data can be dynamically changed, therefore, the problem that whether the user is really interested in the specified category cannot be accurately determined due to low data analysis accuracy in the prior art can be at least partially solved, and the technical effect of improving the data analysis accuracy is further achieved.
Drawings
The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments of the present disclosure with reference to the accompanying drawings, in which:
fig. 1 schematically shows an exemplary system architecture to which the data processing method of the present disclosure and an apparatus thereof may be applied;
FIG. 2 schematically shows a flow chart of a data processing method according to an embodiment of the present disclosure;
FIG. 3A schematically illustrates a flow diagram of a data processing method according to another embodiment of the present disclosure;
FIG. 3B schematically shows a flow diagram of a data processing method according to another embodiment of the present disclosure;
FIG. 4 schematically shows a block diagram of a data processing apparatus according to an embodiment of the present disclosure; and
fig. 5 schematically shows a block diagram of a computer system to which the data processing method of the embodiment of the present disclosure is applied.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The words "a", "an" and "the" and the like as used herein are also intended to include the meanings of "a plurality" and "the" unless the context clearly dictates otherwise. Furthermore, the terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Some block diagrams and/or flow diagrams are shown in the figures. It will be understood that some blocks of the block diagrams and/or flowchart illustrations, or combinations thereof, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the instructions, which execute via the processor, create means for implementing the functions/acts specified in the block diagrams and/or flowchart block or blocks.
Accordingly, the techniques of this disclosure may be implemented in hardware and/or software (including firmware, microcode, etc.). In addition, the techniques of this disclosure may take the form of a computer program product on a computer-readable medium having instructions stored thereon for use by or in connection with an instruction execution system.
The embodiment of the disclosure provides a data processing method and a device thereof. Wherein, the method comprises the following steps: a data acquisition phase and an interest deviation confirmation phase. In the data acquisition stage, first actual operation data of a target user on a specified category and expected operation data of the target user on the specified category need to be acquired, wherein the expected operation data is related to actual operation data generated when a plurality of users operate a plurality of categories and is used as reference data for measuring interest deviation of the target user on the specified category, and the specified category belongs to one of the plurality of categories. In the interest deviation confirming stage, it is necessary to determine the interest deviation of the target user to the specified item according to the acquired first actual operation data and the expected operation data, that is, to determine whether the target user is interested in the specified item.
Fig. 1 schematically shows an exemplary system architecture to which the data processing method and apparatus thereof of the present disclosure can be applied.
As shown in fig. 1, system architecture 100 may include terminal device 101, terminal device 102, terminal device 103, network 104, and server 105. Network 104 is the medium used to provide communication links between terminal device 101, terminal device 102, terminal device 103, and server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
A user may interact with server 105 over network 104 using terminal device 101, terminal device 102, terminal device 103 to receive or send messages, etc. Various communication client applications, such as a shopping application, a web browser application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like, may be installed on the terminal device 101, the terminal device 102, and the terminal device 103, and are not described herein again.
The terminal devices 101, 102, 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a background management server (for example only) providing support for shopping websites browsed by users using the terminal devices 101, 102, and 103. The background management server may analyze and perform other processing on the received data such as the product information query request, and feed back a processing result (e.g., target push information, product information, and the like) to the terminal device.
It should be noted that the data processing method provided by the embodiment of the present disclosure may be executed by the server 105, or may be executed by another server or a server cluster different from the server 105. Accordingly, the data processing apparatus may be provided in the server 105, or may be provided in another server or a server cluster other than the server 105.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
At present, more and more users can choose to trade on an e-commerce platform or other trading platforms, and during the trading process, the system can generate massive trading data, for example, on a shopping website, when users face commodities of different categories, operations such as clicking browsing, commenting and purchasing can be performed on each commodity category (namely categories), and when the operations are performed, the system can generate massive operation data. In the face of massive operation data, it is significant for merchants how to process the data.
Fig. 2 schematically shows a flow chart of a data processing method according to an embodiment of the present disclosure.
As shown in fig. 2, the method may include operations S201 to S203, in which:
in operation S201, first actual operation data of the target user on the designated item is obtained.
According to the embodiment of the disclosure, before processing the data, the operation data of the target user on the specified categories is obtained, wherein the target user may be any specified user, such as a user who can log in a certain transaction website. The specified categories may be categories displayed on a web page. For example, when a user a purchases a certain e-commerce shopping website, different categories of goods, such as fresh goods, clothes, shoes, bags, and the like, exist on a webpage when browsing, and after clicking the item of "clothing", the user a may purchase or collect related clothing under the item of "clothing", and may push information corresponding to the related clothing under the item of "clothing" to other friends, and the like.
It should be noted that, in the embodiment of the present disclosure, the category of the product is not limited, and it may include, but is not limited to, different kinds of products, or categories of the same kind of product in different dimensions. For example, when the product is a short sleeve, the product can be classified into different colors such as red, white, and black. In addition, the commodity classification can also be supermarket in the transaction website, coupon and other classifications. The operation data may be the number of clicks of the target user on the specified category, specifically, as shown in table 1.
TABLE 1
| User ID | Class id | Amount of click |
| A | Mother and infant | 100 |
| B | Book with detachable cover | 50 |
| ... | ... | ... |
| N | Article n | m |
The user ID in table 1 is used to identify each user as a unique identification of the user. The item class id may be a data identification of each item class. The click amount is an operation data amount generated by the user under a certain commodity category, and can be calculated through data pieces in general, for example, the browsing amount of the user A under the mother and baby commodity category is 100. Table 1 may be a data set for data analysis, including N users, N categories, and click through volume. It should be noted that, as the data set for data analysis, one or more data sets may be included, for example, the click volume of the user a on the book, and the click volume of other categories as the data set for data analysis. In addition, the operation data may also be other operation data such as the number of comments on the category, and is not described herein again.
In operation S202, expected operation data of the target user on the designated category is obtained, where the expected operation data is used as reference data for measuring a deviation of interest of the target user on the designated category, and the expected operation data is related to actual operation data generated when a plurality of users operate a plurality of categories, and the designated category belongs to one of the plurality of categories.
According to the embodiment of the disclosure, the expected operation data is used as reference data for measuring the interest deviation of the target user to a certain specified category, and the interest deviation refers to the preference degree of the target user to a certain specified category of commodities. For example, on a trading website, a plurality of users operate on commodities on the website, and whether a target user is interested in a certain commodity can be measured by interest deviation. The expected operation data is related to data generated by a plurality of users operating on a plurality of categories on the website, that is, the expected operation data is changed along with the data generated by the plurality of users operating on the plurality of categories. It can be seen that the desired operational data is variable in relation to data generated by a plurality of users. The target user may be one of the users, or may not be one of the users, and the users may be all users or some users on the website.
In operation S203, a deviation of interest of the target user in the designated category is determined according to the first actual operation data and the expected operation data.
According to the embodiment of the disclosure, whether the target user is interested in the specified category can be determined according to the first actual operation data of the target user on the specified category and the expected operation data related to the data generated by the operation of the plurality of users on the plurality of categories. For example, for a new category or a less common category, the amount of users may be relatively sparse, and in this case, when the deviation of the interests of the users is measured, the users may be mistaken for not being interested in the category. For example, for a commodity that is not common in class a, once a user is interested, the user is interested in the commodity even if he has browsed only a few times. If the scheme provided by the prior art is adopted, the preset value set by people is compared with the actual operation data, rather than the expected value calculated according to the actual situation, so that the interest point of the user is difficult to find due to improper (e.g. excessive) setting of the preset value.
According to the embodiment of the disclosure, the interest deviation of the target user to the specified category is determined according to the actual operation data and the expected operation data of the user to the specified category, wherein the expected operation data is related to the operation data of a plurality of users to a plurality of categories. In this case, it is desirable that the operation data is dynamically changed, and is changed according to the change of the operation data of different categories by a plurality of users. In addition, the analysis method for the user interest preference is not to adopt a univariate statistics on the actual operation data of a certain specified category, but to obtain the actual operation data and the expected operation data of the user on the specified category. Therefore, the problem that whether the user is interested in the specified category cannot be accurately determined due to low data analysis accuracy in the prior art can be at least partially solved, and the technical effect of improving the data analysis accuracy can be further achieved.
The method illustrated in fig. 2 is further described with reference to fig. 3A and 3B in conjunction with specific embodiments.
Fig. 3A schematically shows a flow chart of a data processing method according to another embodiment of the present disclosure. As shown in fig. 3A, the method includes operations S301 to S305, in which:
in operation S301, first actual operation data of the target user on the designated item is obtained.
In operation S302, the probability of occurrence of an event is determined, where the event is an operation performed on a designated category by a plurality of users.
In operation S303, second actual operation data is obtained, where the second actual operation data is a sum of actual operation data generated when the target user operates the multiple categories.
Operation S304 determines desired operation data according to the second actual operation data and the probability of the event occurrence.
In operation S305, a deviation of interest of the target user in the designated item is determined according to the first actual operation data and the expected operation data.
According to the embodiment of the disclosure, determining the probability of the occurrence of the event is determining the probability of the plurality of users in the user group operating the specified item, that is, the probability of the plurality of users operating the specified item on the website is measured by means of statistical probability. The plurality of users may be some users in the user group or all users in the user group. For example, there are user a, user B, user C, and user D in the user group, there are two categories of books and clothing on the shopping website, the probability of determining that a plurality of users operate on the specified category may be the probability of user a, user B, and user C operating on clothing in the user group, and the target user may be user D, user a, or certainly other users besides user a, user B, user C, and user D.
As described above, for example, the target user may be user D, the actual operation data for operating the book is 100 times and the actual operation data for operating the clothing is 200 times, and thus the total of the actual operation data for operating the multiple categories by the target user D is 300 times. The expected operation data can be determined according to the sum of actual operation data generated when the target user D operates the plurality of categories and the probability of the plurality of users in the user group operating the designated categories. And then, after the expected operation data and the actual operation data of the target user are obtained, whether the target user is interested in the specified category or not is determined according to the actual operation data and the expected operation data of the target user.
According to the embodiment of the disclosure, since the prior art adopts a univariate or single statistical method to analyze data, in this case, it is difficult to distinguish whether users are interested in a certain category when the same data appears, for example, user a and user B click for the same commodity for 2 times, in this case, it is difficult to measure the true interest deviation of the two users. According to the embodiment of the disclosure, the operation data (i.e., the second actual operation data) of the target user for the plurality of classifications may be multiplied by the probability that the plurality of users operate the designated classification, so as to obtain the desired operation data. The operation data of the target user on the multiple categories and the probability of the target user on the operation on the specified categories are considered at the same time, and the obtained expected operation data is compared with the actual operation data, so that the problem that the real interest deviation of the multiple users cannot be measured when the multiple users click the same commodity for the same times can be solved.
According to an embodiment of the present disclosure, wherein determining the probability of the event occurrence comprises: acquiring third actual operation data, wherein the third actual operation data comprises the sum of actual operation data generated when a plurality of users operate a plurality of categories; acquiring fourth actual operation data, wherein the fourth actual operation data comprises the sum of actual operation data generated when a plurality of users operate the specified category; and determining the ratio of the fourth actual operation data to the third actual operation data to obtain the probability of the occurrence of the event.
According to the embodiment of the disclosure, in order to determine the probability of the operation of the plurality of users in the user group on a certain specified category, the probability may be used as a probability quantization index of the operation of each user in the user group on the specified category, and each user may be one of the plurality of users, may also be another user other than the plurality of users, and may of course also be a target user. The probability may be a ratio of a total sum of actual operation data generated when the plurality of users operate the designated category to a total sum of actual operation data generated when the plurality of users operate the plurality of categories. For example, the operation data obtained by the N users operating the N categories displayed on the web page may be obtained by using an aggregation method to obtain the third actual operation data, which is the sum of the operation data obtained by the N categories (a plurality of or all of the categories) displayed on the web page by a plurality of or all of the N users. The fourth actual operation data may be a total operation data obtained by performing an operation on a designated category displayed on the web page by a plurality of or all of the N users by using an aggregation method.
Specifically, for example, the probability P that the users in the user group i operate in the category j is calculatedijThe probability can reflect the possibility that the user operates on the category j.
Pij＝Nj/Nall
Wherein N isjThe data size generated by operating all users in the user group on the category j is provided, so that the heat of the category j is fed back in the magnitude; n is a radical ofallThe amount of data generated for all users in the user group to operate on all categories. And measuring the activity of the user in operating on the category according to the ratio of the data volume of one category to the data volume of all the categories.
According to the embodiment of the present disclosure, the probability obtained by the above method may be used to calculate the expected operation data, specifically, for example, the sum of actual operation data generated when the target user operates a plurality of categories is multiplied by the probability to obtain the standard expected data amount generated by the target user in a specified category relative to the entire user group, that is, the expected operation data. By the method, the expected operation data is closely related to the probability that all or part of the users operate under the specified category, and meanwhile, the actual operation data of the target users operating in the website is considered.
According to an embodiment of the present disclosure, wherein determining the target user's interest bias for the specified category according to the first actual operation data and the expected operation data comprises: judging whether the numerical value corresponding to the first actual operation data is larger than the numerical value corresponding to the expected operation data; and if so, determining that the target user is interested in the specified category.
According to the embodiment of the disclosure, the operation data of the target user on the specified category is compared with the expected operation data, and under the condition that the operation data of the target user on the specified category is larger than the value corresponding to the expected operation data, the target user can be judged to be interested in the specified category. When the operation data of the target user on the designated category is smaller than the numerical value corresponding to the expected operation data, it can be determined that the target user is not interested in the designated category or is not interested in a high degree.
According to an embodiment of the present disclosure, wherein determining the target user's interest bias for the specified category according to the first actual operation data and the expected operation data comprises: calculating interest deviation values of the target users to the specified categories according to the first actual operation data and the expected operation data; and determining the interest deviation of the target user to the specified category according to the interest deviation value.
When the users in the user group operate different categories, under the condition that the operation times of each category are increased, if only whether the operation of the users on the specified categories is larger than a preset value is analyzed, whether the users are interested in the specified categories is obviously not represented.
According to the embodiment of the disclosure, the actual operation data of the user on the specified category may be not only the click rate but also the number of comments, and may also be the content of the user commenting on the specified category, and the corresponding expected operation data may also be the click rate, the number of comments, and the content of the user commenting on the specified category. When the operation data is the content commenting on the specified categories, whether the user is interested in the specified categories or not can be determined according to the semantic relation between the commented contents. When the operation data is other quantized data such as click quantity for operating the specified category, an interest deviation value of the target user for the specified category can be calculated according to the first actual operation data and the expected operation data, so that whether the user is interested in the specified category or not is quantized.
According to an embodiment of the present disclosure, wherein calculating the interest deviation value of the target user for the specified category according to the first actual operation data and the expected operation data comprises: determining an adjustment coefficient according to the first actual operation data and the expected operation data, wherein the adjustment coefficient is used for adjusting the interest deviation value; and calculating interest deviation values of the target users for the specified categories according to the first actual operation data, the expected operation data and the adjusting coefficients.
In accordance with embodiments of the present disclosure, specifically, for example, by interest bias valuesijMeasuring the interest deviation of the target user i to the specified category j, wherein the formula is as follows:
where K is an adjustment factor, optionally when the first actual operation data NijLarger or expected operational data EijThe relative adjustment value is carried out in a small case, and in general, the empirical value of the adjustment coefficient K may be 0.22. The core judgment criterion of the above formula is NijAnd EijWhen is coming into contact withijIs more than 1 and is equivalent to logN under the condition that the adjusting coefficient K is not adjustedij≥logEij+
Wherein
Can be used to adjust the desired operational data EijLess significant cases resulting from too small a value, with EijThe increase in the number of the first and second,
the adjustment of (a) is negligible. The unobvious significance means that the interest degree of the user in the commodity is not obvious.
For example, by interest bias value γijAs the interest score of the target user i on the designated category j, the formula is as follows:
interest bias value γijAs the interest measurement of the target user i on the designated item j, the higher the score is, the higher the target user i on the designated item isThe higher the interest level of class j. Can be biased by interestiiAnd carrying out reverse order arrangement to screen the user groups.
According to the embodiment of the disclosure, since in large-scale machine learning, feature data and algorithms are equally important to the recognition capability of the model. The interest and preference of a user to a certain specific category has better resolution capability on the algorithm model and can be directly transmitted to the algorithm model as a characteristic value. By quantifying the interest deviation of the user to the specified category, the method can be used as the measure of the interest deviation degree of the user to the specified category, thereby providing more accurate characteristic values for machine learning, advertisement recommendation during online user transaction and the like.
Fig. 3B schematically shows a flow chart of a data processing method according to another embodiment of the present disclosure. As shown in fig. 3B, the method includes operations S401 to S405, in which:
in operation S401, first actual operation data of the target user on the designated item is obtained.
In operation S402, expected operation data of the target user on the designated category is obtained, where the expected operation data is used as reference data for measuring a deviation of interest of the target user on the designated category, and the expected operation data is related to actual operation data generated when a plurality of users operate a plurality of categories, and the designated category belongs to one of the plurality of categories.
In operation S403, a deviation of interest of the target user in the designated category is determined according to the first actual operation data and the expected operation data.
In operation S404, it is determined whether the determined interest deviation of the target user for the designated category meets the expected index.
In operation S405, if it is determined that the interest deviation of the target user for the designated category does not meet the expected index, filtering out the first actual operation data.
According to an embodiment of the present disclosure, after determining the interest deviation of the target user for the specified category according to the first actual operation data and the expected operation data, the method further includes: judging whether the determined interest deviation of the target user to the specified categories meets expected indexes or not; and filtering out first actual operation data when the determined interest deviation of the target user to the specified category does not accord with the expected index. And when the determined interest deviation of the target user to the specified category meets the expected index, reserving the first actual operation data. Since data that does not meet the expected target is likely to affect the interest evaluation of the better user to a greater extent (the better user is the user who produced the data that meets the expected target). In addition, data that does not meet the expectation index also represents a degree of interest that is difficult to objectively judge the user to a certain degree. Through the embodiment of the disclosure, data which do not accord with expected indexes can be filtered, the interest degree of a user can be objectively judged, and the accuracy of data analysis is further improved.
It should be noted that the expected index may be a quantization index, and different expected indexes may be obtained according to different interest bias determination methods, for example, by using the above interest bias valueijWhen the interest degree of the user is measured, the interest deviation value is calculatedijComparing with 1, the expected index is 1. The expected index may also be other qualitative indexes, for example, the interest bias of different users may be like, general, or when dislike, words with similar semantics to like may be used as the expected index. The deviation of interest that does not meet the expected criteria is general and disliked.
Fig. 4 schematically shows a block diagram of a data processing device according to an embodiment of the present disclosure.
As shown in fig. 4, the data processing apparatus 500 includes a first obtaining module 510, a second obtaining module 520, and a first determining module 530.
The first obtaining module 510 is configured to obtain first actual operation data of the target user on the specified item.
The second obtaining module 520 is configured to obtain expected operation data of the target user on the designated category, where the expected operation data is used as reference data for measuring a deviation of interest of the target user on the designated category, and the expected operation data is related to actual operation data generated when a plurality of users operate a plurality of categories, and the designated category belongs to one of the plurality of categories.
The first determining module 530 is configured to determine a deviation of interest of the target user in the designated category according to the first actual operation data and the expected operation data.
According to the embodiment of the disclosure, the deviation of the user interest in the specified category is determined according to the actual operation data and the expected operation data of the user on the specified category, wherein the expected operation data is related to the operation data of a plurality of users on a plurality of categories. In this case, it is desirable that the operation data is changeable due to the change of the operation data for different categories by a plurality of users, that is, it is desirable that the operation data is dynamically changeable. In addition, the analysis method for the user interest preference is not to adopt a univariate statistics on the actual operation data of a certain specified category, but to obtain the actual operation data and the expected operation data of the user on the specified category. Therefore, the problem that whether the user really is interested in the specified category cannot be accurately determined due to low data analysis accuracy in the prior art can be at least partially solved, and the technical effect of improving the data analysis accuracy is further achieved.
According to an embodiment of the present disclosure, the second obtaining module includes: the first determining unit is used for determining the probability of occurrence of an event, wherein the event is the operation of a plurality of users on a specified category; the acquiring unit is used for acquiring second actual operation data, wherein the second actual operation data is the sum of actual operation data generated when the target user operates the multiple categories; and a second determination unit for determining the expected operation data according to the second actual operation data and the probability of the event occurrence.
According to an embodiment of the present disclosure, the first determining unit includes: the first acquiring subunit is used for acquiring third actual operation data, wherein the third actual operation data comprises the sum of actual operation data generated when a plurality of users operate a plurality of categories; the second acquiring subunit is used for acquiring fourth actual operation data, wherein the fourth actual operation data comprises the sum of actual operation data generated when a plurality of users operate the specified category; and the first determining subunit is used for determining the ratio of the fourth actual operation data to the third actual operation data to obtain the probability of the occurrence of the event.
According to an embodiment of the present disclosure, wherein the first determining module includes: the judging unit is used for judging whether the numerical value corresponding to the first actual operation data is larger than the numerical value corresponding to the expected operation data; and the second determining unit is used for determining that the target user is interested in the specified category under the condition that the numerical value corresponding to the first actual operation data is larger than the numerical value corresponding to the expected operation data.
According to an embodiment of the present disclosure, the first determining module includes: the calculating unit is used for calculating interest deviation values of the target users to the specified categories according to the first actual operation data and the expected operation data; and the third determining unit is used for determining the interest deviation of the target user to the specified category according to the interest deviation value.
According to an embodiment of the present disclosure, wherein the calculating unit includes: a second determining subunit, configured to determine an adjustment coefficient according to the first actual operation data and the expected operation data, where the adjustment coefficient is used to adjust the interest deviation value; and the calculating subunit is used for calculating the interest deviation value of the target user to the specified category according to the first actual operation data, the expected operation data and the adjusting coefficient.
According to the embodiment of the present disclosure, wherein, the apparatus further comprises: the judging module is used for judging whether the determined interest deviation of the target user to the specified category meets the expected index or not after the interest deviation of the target user to the specified category is determined according to the first actual operation data and the expected operation data; and the filtering module is used for filtering the first actual operation data under the condition that the judged interest deviation of the target user to the specified category accords with the expected index.
It should be noted that the data processing apparatus and the data processing method in the embodiments of the present disclosure correspond to each other, and for the description of the data processing apparatus, reference may be made to the description of the data processing method in the embodiments of the present disclosure, and details are not repeated here.
According to an embodiment of the present disclosure, there is provided a data processing system including, a computer-readable storage medium; a processor.
Another aspect of the disclosure provides a computer program comprising computer executable instructions for implementing the method as described above when executed.
Fig. 5 schematically shows a block diagram of a computer system to which the data processing method of the embodiment of the present disclosure is applied.
FIG. 5 schematically illustrates a block diagram of a computer system 600 suitable for use in implementing embodiments of the present disclosure.
As shown in fig. 5, the computer system 600 includes a central processing unit (CPU 601) that can perform various appropriate actions and processes according to a program stored in a read only memory (ROM 602) or a program loaded from a storage section 608 into a random access memory (RAM 603). In the RAM 603, various programs and data necessary for the operation of the computer system 600 are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input/output interface (I/O interface 605) is also connected to bus 604.
To the I/O interface 605, AN input section 606 including a keyboard, a mouse, and the like, AN output section 607 including a network interface card such as a Cathode Ray Tube (CRT), a liquid crystal display (L CD), and the like, a speaker, and the like, a storage section 608 including a hard disk, and the like, and a communication section 609 including a network interface card such as a L AN card, a modem, and the like, the communication section 609 performs communication processing via a network such as the internet, a drive 610 is also connected to the I/O interface 605 as necessary, a removable medium 611 such as a magnetic disk, AN optical disk, a magneto-optical disk, a semiconductor memory, and the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted into the storage section 608 as necessary.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure 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 illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program, when executed by the CPU 601, performs the above-described functions defined in the system of the present disclosure.
It should be noted that the computer readable media shown in the present disclosure may be computer readable signal media or computer readable storage media or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination 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 present disclosure, 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 contrast, in the present disclosure, a computer-readable signal medium may include a propagated data signal with computer-readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. 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 flowchart 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 disclosure. 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 described in the embodiments of the present disclosure may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes an acquisition unit, a determination unit, and a judgment unit. Here, the names of the units do not constitute a limitation to the unit itself in some cases, and for example, the first acquiring unit may also be described as a "unit that acquires first actual operation data of the target user on the specified item".
As another aspect, a computer-readable medium is also provided according to an embodiment of the present disclosure. The computer-readable medium carries one or more programs which, when executed, implement a data processing method according to an embodiment of the present disclosure, including: acquiring first actual operation data of a target user on a specified category; acquiring expected operation data of a target user on an appointed category, wherein the expected operation data is used as reference data for measuring interest deviation of the target user on the appointed category, the expected operation data is related to actual operation data generated when a plurality of users operate the plurality of categories, and the appointed category belongs to one of the plurality of categories; and determining the interest deviation of the target user to the specified category according to the first actual operation data and the expected operation data.
The method for acquiring the expected operation data of the target user on the specified categories comprises the following steps: determining the occurrence probability of an event, wherein the event is the operation of a plurality of users on a specified category; acquiring second actual operation data, wherein the second actual operation data is the sum of actual operation data generated when the target user operates the multiple categories; and determining expected operation data according to the second actual operation data and the probability of the event occurrence. Determining the probability of the event occurring comprises: acquiring third actual operation data, wherein the third actual operation data comprises the sum of actual operation data generated when a plurality of users operate a plurality of categories; acquiring fourth actual operation data, wherein the fourth actual operation data comprises the sum of actual operation data generated when a plurality of users operate the specified category; and determining the ratio of the fourth actual operation data to the third actual operation data to obtain the probability of the occurrence of the event. Determining a deviation of interest of the target user in the specified category according to the first actual operation data and the expected operation data comprises: judging whether the numerical value corresponding to the first actual operation data is larger than the numerical value corresponding to the expected operation data; and if so, determining that the target user is interested in the specified category. Determining a deviation of interest of the target user in the specified category according to the first actual operation data and the expected operation data comprises: calculating interest deviation values of the target users to the specified categories according to the first actual operation data and the expected operation data; and determining the interest deviation of the target user to the specified category according to the interest deviation value. Calculating interest deviation values of the target users to the specified categories according to the first actual operation data and the expected operation data comprises the following steps: determining an adjustment coefficient according to the first actual operation data and the expected operation data, wherein the adjustment coefficient is used for adjusting the interest deviation value; and calculating interest deviation values of the target users for the specified categories according to the first actual operation data, the expected operation data and the adjusting coefficients. After determining the target user's interest bias for the specified category based on the first actual operational data and the desired operational data, the method further comprises: judging whether the determined interest deviation of the target user to the specified categories meets expected indexes or not; and if not, filtering the first actual operation data.
The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.
Claims (14)
1. A method of data processing, comprising:
acquiring first actual operation data of a target user on a specified category;
acquiring expected operation data of the target user on the specified category, wherein the expected operation data is used as reference data for measuring interest deviation of the target user on the specified category, the expected operation data is related to actual operation data generated when a plurality of users operate a plurality of categories, and the specified category belongs to one of the plurality of categories;
determining interest deviation of the target user to the specified category according to the first actual operation data and the expected operation data;
wherein the obtaining of the expected operation data of the target user on the specified category comprises:
determining the probability of occurrence of an event, wherein the event is the operation of the plurality of users on the specified category;
acquiring second actual operation data, wherein the second actual operation data is the sum of actual operation data generated when the target user operates the multiple categories; and
and determining the expected operation data according to the second actual operation data and the probability of the event occurrence.
2. The method of claim 1, wherein determining a probability of an event occurring comprises:
acquiring third actual operation data, wherein the third actual operation data comprises the sum of actual operation data generated when the plurality of users operate the plurality of categories;
acquiring fourth actual operation data, wherein the fourth actual operation data comprises the sum of actual operation data generated when the plurality of users operate the specified category; and
and determining the ratio of the fourth actual operation data to the third actual operation data to obtain the probability of the occurrence of the event.
3. The method of claim 1, wherein determining the target user's interest bias for the specified category based on the first actual operational data and the desired operational data comprises:
judging whether the numerical value corresponding to the first actual operation data is larger than the numerical value corresponding to the expected operation data; and
and if so, determining that the target user is interested in the specified category.
4. The method of claim 1, wherein determining the target user's interest bias for the specified category based on the first actual operational data and the desired operational data comprises:
calculating interest deviation values of the target users to the specified categories according to the first actual operation data and the expected operation data; and
and determining the interest deviation of the target user to the specified category according to the interest deviation value.
5. The method of claim 4, wherein calculating the target user interest bias value for the specified category based on the first actual operational data and the desired operational data comprises:
determining an adjustment coefficient according to the first actual operation data and the expected operation data, wherein the adjustment coefficient is used for adjusting the interest deviation value; and
and calculating interest deviation values of the target users to the specified categories according to the first actual operation data, the expected operation data and the adjusting coefficients.
6. The method of any of claims 1 to 5, wherein after determining the target user's interest bias for the specified category based on the first actual operational data and the desired operational data, the method further comprises:
judging whether the determined interest deviation of the target user to the specified categories meets expected indexes or not; and
and if not, filtering the first actual operation data.
7. A data processing apparatus comprising:
the first acquisition module is used for acquiring first actual operation data of a target user on a specified category;
a second obtaining module, configured to obtain expected operation data of the target user on the specified category, where the expected operation data is used as reference data for measuring a deviation of interest of the target user on the specified category, and the expected operation data is related to actual operation data generated when multiple users operate multiple categories, and the specified category belongs to one of the multiple categories;
a first determining module, configured to determine, according to the first actual operation data and the expected operation data, a deviation of interest of the target user in the specified category;
wherein the second obtaining module comprises:
a first determining unit, configured to determine a probability of occurrence of an event, where the event is an operation performed on the specified category by the multiple users;
the acquiring unit is used for acquiring second actual operation data, wherein the second actual operation data is the sum of actual operation data generated when the target user operates the multiple categories; and
a second determining unit, configured to determine the expected operation data according to the second actual operation data and the probability of the event occurrence.
8. The apparatus of claim 7, wherein the first determining unit comprises:
the first acquiring subunit is configured to acquire third actual operation data, where the third actual operation data includes a sum of actual operation data generated when the plurality of users operate the plurality of categories;
a second obtaining subunit, configured to obtain fourth actual operation data, where the fourth actual operation data includes a sum of actual operation data generated when the plurality of users operate the specified category; and
and the first determining subunit is configured to determine a ratio of the fourth actual operation data to the third actual operation data, so as to obtain a probability of the event.
9. The apparatus of claim 7, wherein the first determining means comprises:
the judging unit is used for judging whether the numerical value corresponding to the first actual operation data is larger than the numerical value corresponding to the expected operation data; and
and a second determining unit, configured to determine that the target user is interested in the specified category when the value corresponding to the first actual operation data is greater than the value corresponding to the expected operation data.
10. The apparatus of claim 7, wherein the first determining means comprises:
the calculating unit is used for calculating an interest deviation value of the target user to the specified category according to the first actual operation data and the expected operation data; and
and the third determining unit is used for determining the interest deviation of the target user to the specified category according to the interest deviation value.
11. The apparatus of claim 10, wherein the computing unit comprises:
a second determining subunit, configured to determine an adjustment coefficient according to the first actual operation data and the expected operation data, where the adjustment coefficient is used to adjust the interest deviation value; and
and the calculating subunit is used for calculating the interest deviation value of the target user to the specified category according to the first actual operation data, the expected operation data and the adjusting coefficient.
12. The apparatus of any of claims 7 to 11, wherein the apparatus further comprises:
the judging module is used for judging whether the determined interest deviation of the target user to the specified category meets an expected index or not after the interest deviation of the target user to the specified category is determined according to the first actual operation data and the expected operation data; and
and the filtering module is used for filtering the first actual operation data under the condition that the judged interest deviation of the target user to the specified category accords with an expected index.
13. A computer readable storage medium having stored thereon executable instructions for implementing the data processing method of any one of claims 1 to 6 when executed by a processor.
14. A data processing system comprising:
the computer-readable storage medium of claim 13; and
the processor of claim 13.
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