CN111784091A - Method and apparatus for processing information - Google Patents
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- CN111784091A CN111784091A CN201910886440.4A CN201910886440A CN111784091A CN 111784091 A CN111784091 A CN 111784091A CN 201910886440 A CN201910886440 A CN 201910886440A CN 111784091 A CN111784091 A CN 111784091A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0281—Customer communication at a business location, e.g. providing product or service information, consulting
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0631—Item recommendations
Abstract
The embodiment of the application discloses a method and a device for processing information. One embodiment of the method comprises: acquiring related characteristic information for scoring a target object, wherein the related characteristic information comprises article characteristic information of a target article and/or user characteristic information of a target user, the target article comprises an article recommended by the target object in a group, and the target user comprises a user in the same group with the target object and/or a user for evaluating the target object; inputting the relevant characteristic information into a pre-trained target scoring model to obtain a target score of a target object; and processing the object identification of the target object based on the target score. The method and the device can more accurately determine the score of the target object, so that the object identification of the target object can be more specifically processed.
Description
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to a method and a device for processing information.
Background
With the development of big data and internet, community group buying plays an increasingly important role in economic activities. The community group purchase is a new form of network group purchase, and is a way for group leaders to organize consumers to purchase commodities at low price in a virtual community. Community group buying provides some aggregated consumers on one hand and enables collaborative mutual assistance among users on the other hand. In the community group purchase process, the group leader plays a very important role, so that it is meaningful to evaluate the service quality of the group leader.
Disclosure of Invention
The embodiment of the application provides a method and a device for processing information.
In a first aspect, an embodiment of the present application provides a method for processing information, including: acquiring related characteristic information for scoring a target object, wherein the related characteristic information comprises article characteristic information of a target article and/or user characteristic information of a target user, the target article comprises an article recommended by the target object in a group, and the target user comprises a user in the same group with the target object and/or a user for evaluating the target object; inputting the relevant characteristic information into a pre-trained target scoring model to obtain a target score of a target object; and processing the object identification of the target object based on the target score.
In some embodiments, the method further comprises: in response to receiving an item image presented with a target item, generating an evaluation link based on the item image, and pushing the evaluation link; and receiving evaluation information of the target item presented by the user aiming at the item image as user characteristic information, wherein the evaluation information is generated by the user performing evaluation operation in the page indicated by the evaluation link.
In some embodiments, the item characteristic information of the target item comprises at least one of: the number of browsed times corresponding to the target item, the number of clicked times corresponding to the target item, the number of paid attention corresponding to the target item, the number of times the item information of the target item is added to the target item information set, and the price of the target item.
In some embodiments, the related feature information includes at least one item identifier, the at least one item identifier includes an item identifier of the target item and/or an item identifier of an item evaluated by the target user, the at least one item identifier belongs to at least one item identifier set, and each item identifier set in the at least one item identifier set corresponds to a pre-trained scoring model; and before inputting the relevant characteristic information into a pre-trained target scoring model to obtain a target score of the target object, the method further comprises the following steps: for each item identification set in at least one item identification set, determining the number of item identifications belonging to the item identification set in at least one item identification set; and determining the pre-trained scoring model corresponding to the maximum number of item identification sets as a target scoring model.
In some embodiments, the set of item identifications corresponding to the maximum number corresponds to at least two pre-trained scoring models; and inputting the relevant characteristic information into a pre-trained target scoring model to obtain a target score of the target object, wherein the method comprises the following steps: for each determined at least two target scoring models, inputting relevant feature information into the target scoring model to obtain an initial score of a target object; and determining a target score of the target object based on the initial scores output by each of the at least two target scoring models.
In some embodiments, processing the object identification of the target object based on the target score includes: and in response to determining that the target score is greater than a preset first score threshold, adding the preferential information corresponding to the target score to an account associated with the object identifier.
In some embodiments, processing the object identification of the target object based on the target score includes: and in response to determining that the target score is less than the preset second score threshold, adding the object identifier of the target object to a preset object identifier set.
In a second aspect, an embodiment of the present application provides an apparatus for processing information, including: the system comprises an acquisition unit, a storage unit and a processing unit, wherein the acquisition unit is configured to acquire related characteristic information for scoring a target object, wherein the related characteristic information comprises article characteristic information of a target article and/or user characteristic information of a target user, the target article comprises an article recommended by the target object in a group, and the target user comprises a user in the same group with the target object and/or a user evaluating the target object; the input unit is configured to input the relevant characteristic information into a pre-trained target scoring model to obtain a target score of the target object; a processing unit configured to process the object identification of the target object based on the target score.
In some embodiments, the apparatus further comprises: a pushing unit configured to generate an evaluation link based on an item image in response to receiving the item image presented with the target item, and push the evaluation link; and the receiving unit is configured to receive evaluation information of the target item presented by the user for the item image as the user characteristic information, wherein the evaluation information is generated by the user performing evaluation operation in the page indicated by the evaluation link.
In some embodiments, the item characteristic information of the target item comprises at least one of: the number of browsed times corresponding to the target item, the number of clicked times corresponding to the target item, the number of paid attention corresponding to the target item, the number of times the item information of the target item is added to the target item information set, and the price of the target item.
In some embodiments, the related feature information includes at least one item identifier, the at least one item identifier includes an item identifier of the target item and/or an item identifier of an item evaluated by the target user, the at least one item identifier belongs to at least one item identifier set, and each item identifier set in the at least one item identifier set corresponds to a pre-trained scoring model; and the apparatus further comprises: a first determining unit configured to determine, for each item identification set of at least one item identification set, a number of item identifications of the at least one item identification set that belong to the item identification set; and the second determination unit is configured to determine the pre-trained scoring model corresponding to the maximum number of item identification sets as the target scoring model.
In some embodiments, the set of item identifications corresponding to the maximum number corresponds to at least two pre-trained scoring models; and the input unit is further configured to input the relevant feature information into a pre-trained target scoring model to obtain a target score of the target object as follows: for each determined at least two target scoring models, inputting relevant feature information into the target scoring model to obtain an initial score of a target object; and determining a target score of the target object based on the initial scores output by each of the at least two target scoring models.
In some embodiments, the processing unit is further configured to process the object identification of the target object based on the target score as follows: and in response to determining that the target score is greater than a preset first score threshold, adding the preferential information corresponding to the target score to an account associated with the object identifier.
In some embodiments, the processing unit is further configured to process the object identification of the target object based on the target score as follows: and in response to determining that the target score is less than the preset second score threshold, adding the object identifier of the target object to a preset object identifier set.
In a third aspect, an embodiment of the present application provides an electronic device, including: one or more processors; a storage device, on which one or more programs are stored, which, when executed by the one or more processors, cause the one or more processors to implement the method as described in any implementation manner of the first aspect.
In a fourth aspect, the present application provides a computer-readable medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method as described in any implementation manner of the first aspect.
According to the method and the device for processing information provided by the embodiment of the application, the relevant characteristic information for scoring the target object is firstly acquired; then, inputting the relevant characteristic information into a pre-trained target scoring model to obtain a target score of the target object; and finally, processing the object identification of the target object based on the target score. By the method, the score of the target object can be determined more accurately, so that the object identification of the target object can be processed more specifically.
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Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is an exemplary system architecture diagram in which various embodiments of the present application may be applied;
FIG. 2 is a flow diagram for one embodiment of a method for processing information according to the present application;
FIG. 3 is a schematic diagram of an application scenario of a method for processing information according to the present application;
FIG. 4 is a flow diagram of yet another embodiment of a method for processing information according to the present application;
FIG. 5 is a schematic block diagram illustrating one embodiment of an apparatus for processing information according to the present application;
FIG. 6 is a schematic block diagram of a computer system suitable for use in implementing an electronic device according to embodiments of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
FIG. 1 illustrates an exemplary system architecture 100 to which embodiments of the method for processing information of the present application may be applied.
As shown in fig. 1, the system architecture 100 may include terminal devices 1011, 1012, 1013, a network 102, and a server 103. Network 102 is the medium used to provide communication links between terminal devices 1011, 1012, 1013 and server 103. Network 102 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 1011, 1012, 1013 to interact with the server 103 through the network 102 to send or receive a message or the like, for example, the terminal devices 1011, 1012, 1013 may send related feature information for scoring a target object, such as evaluation information of the target object by the user, to the server 103. Various communication client applications, such as shopping applications, instant messaging software, image processing applications, and the like, may be installed on the terminal devices 1011, 1012, 1013.
The terminal devices 1011, 1012, 1013 may be hardware or software. When the terminal devices 1011, 1012, 1013 are hardware, they may be various electronic devices having speakers and supporting information interaction, including but not limited to smart phones, tablet computers, laptop computers, and the like. When the terminal devices 1011, 1012, 1013 are software, they may be installed in the electronic devices listed above. It may be implemented as multiple pieces of software or software modules (e.g., multiple pieces of software or software modules to provide distributed services) or as a single piece of software or software module. And is not particularly limited herein.
The server 103 may be a server that provides various services. For example, the server may process the related feature information for scoring the target object, such as the evaluation information of the target object sent by the terminal devices 1011, 1012, 1013. The server 103 may first obtain relevant feature information for scoring the target object; then, the related characteristic information can be input into a pre-trained target scoring model to obtain a target score of the target object; finally, the object id of the target object may be processed based on the target score.
The server 103 may be hardware or software. When the server 103 is hardware, it may be implemented as a distributed server cluster composed of a plurality of servers, or may be implemented as a single server. When the server 103 is software, it may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services), or as a single piece of software or software module. And is not particularly limited herein.
It should be noted that the method for processing information provided in the embodiment of the present application is generally performed by the server 103.
It should be noted that the server 103 may also locally store relevant feature information for scoring the target object, and the server 103 may locally acquire the relevant feature information for scoring the target object. The exemplary system architecture 100 may not have the network 102 and the terminal devices 1011, 1012, 1013 at this time.
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.
With continued reference to FIG. 2, a flow 200 of one embodiment of a method for processing information in accordance with the present application is shown. The method for processing information comprises the following steps:
In the present embodiment, an execution subject (e.g., a server shown in fig. 1) of the method for processing information may acquire relevant feature information for scoring a target object. The target object may be a user in the group who makes item recommendation to group members, for example, may be a manager in the group.
In this embodiment, the related feature information may include article feature information of the target article and/or user feature information of the target user. The target object may include an object recommended by the target object in a group. The item characteristic information of the target item may include, but is not limited to, at least one of: the number of times the target item was purchased by the user and the category to which the target item belongs. The target users may include users in the same group as the target object and/or users who rate the target object. The user characteristic information of the target user may include, but is not limited to, at least one of: the evaluation information of the user on the target object and the activity degree of the user in the group. The user can generate the evaluation information of the article in a star rating or character evaluation mode. Here, the evaluation information may be sent by the user in the group, or may be issued by the user on a preset platform. The above evaluation information may include, but is not limited to, at least one of: the evaluation information of the user on the price of the article, the evaluation information of the user on the quality of the article and the evaluation information of the target object recommending the article. The activity level of the user in the group may include the number of pieces of information sent by the user within a preset time period (for example, 20 minutes) after the target object makes item recommendation in the group.
In this embodiment, the execution subject may filter the acquired relevant feature information to delete sensitive data in the acquired relevant feature information. As an example, the execution main body may store a sensitive data table, and the execution main body may determine whether there is data in the sensitive data table in the acquired relevant feature information, and if so, filter out the data in the sensitive data table.
In this embodiment, the executing body may further execute the following processing on the acquired relevant feature information: truncation processing and/or missing value processing. For continuous numerical features, feature information can be truncated on the premise of retaining important information. Missing value processing may include two ways: the first is to complement a value, such as a mean or median, and the second is to encode the missing as a message and feed it back to the model for learning.
In this embodiment, the executing entity may input the relevant feature information acquired in step 201 into a pre-trained target scoring model to obtain a target score of the target object. Here, the target scoring model may be used to represent a correspondence between the related feature information for scoring the object and a target score of the object, and the electronic device may train the target scoring model that may represent a correspondence between the related feature information for scoring the object and the target score of the object in various ways. The target fraction may be between 0 and 1, between 0 and 10, or between 0 and 100. The target scores described above may also be characterized as score grades, e.g., grade a, grade B, grade C, grade D, and grade E.
As an example, the target scoring model may be a correspondence table in which a plurality of correspondences between relevant feature information for scoring an object and a target score of the object are stored, which is prepared in advance by a technician based on statistics of a large amount of relevant feature information for scoring an object and the target score of the corresponding object. The target scoring model may also be a calculation formula that is preset by a technician based on statistics of a large amount of data and stored in the electronic device, and performs numerical calculation on one or more values in the related feature information to obtain a target score of the object, for example, the calculation formula may be a formula that multiplies a sum of a number of times that the target item is purchased by the user and a number of pieces of information sent by the user within a preset time period after the object recommends an item in the group by a preset coefficient, and the product may be used to represent the target score of the object. It should be noted that the preset coefficient can be used to adjust the obtained product to a preset fraction segment, for example, to a fraction segment of 0 to 100, or to a fraction segment of 0 to 10.
As another example, the objective scoring model may be obtained by training the executing entity or other executing entities for training the objective scoring model by:
in step S1, a sample set may be obtained, where the sample may include related feature information for scoring the object and a target score of the object corresponding to the related feature information.
In step S2, the initial model may be trained to obtain a target scoring model by taking the relevant feature information for scoring the object in each sample set as input and the target score of the object corresponding to the relevant feature information for scoring the object in each sample set as output. In this way, the relevant feature information for scoring the object can be input from the input side of the initial model, subjected to the processing of the parameters of each layer in the initial model, and output from the output side of the initial model, where the information output by the output side is the target score of the object.
Here, the initial model may be a Logistic Regression model (LR), a Factorization Machine (FM), a Gradient Boosting Decision Tree (GBDT), and a Deep Neural Network (DNN). The logistic regression model is a generalized linear model, and the result of the linear function is mapped through a logarithmic probability function, so that the value space of the target function is mapped to (0, 1). The factorization machine can automatically make feature combinations to process high-dimensional sparse features. The gradient lifting tree is an integrated learning method based on a regression tree, a plurality of weak regression trees are constructed to serve as a base learner, the results of the trees are accumulated to serve as final prediction output, a forward distribution algorithm is adopted during training, the value of fitting of a first tree is firstly determined, then the next tree is updated and trained on the basis of errors of all previous trees, and iteration is carried out step by step until the whole model is constructed.
And step 203, processing the object identification of the target object based on the target score.
In this embodiment, the execution subject may process the object identifier of the target object based on the target score. Specifically, the execution subject may first determine whether the target score is greater than a preset third score threshold. The third score threshold may be set according to a score range of the target score, and may be, for example, a product of a maximum score in the score range of the target score and a preset third ratio. Here, assuming that the preset third ratio is 0.9, if the score range of the target score is 0 to 1, the third score threshold may be 0.9; if the score range of the target score is 0 to 10, the third score threshold may be 9. If it is determined that the target score is greater than the third score threshold, the execution subject may add the object identifier to a set of target object identifiers. The object indicated by the target object identifier in the target object identifier set has a preset right, for example, the right to select the recommended item may be prioritized.
In some optional implementations of the embodiment, the execution subject may determine whether an item image presented with the target item is received. The user may take an image of the purchased target item and send the taken image of the item presented with the target item to the execution body. When the execution body receives an article image in which a target article is presented, an evaluation link may be generated based on the article image. Specifically, a page including the item image may be generated, and then an evaluation link corresponding to the page may be generated. After the user clicks the evaluation link at the user terminal, the user terminal may present a page including the item image, and the user may evaluate the target item presented by the item image in the page. Then, the execution agent may push the evaluation link. In response to detecting the click operation of the user on the preset icon, the execution main body may push the evaluation link to the user terminal of the user who executes the click operation. The icon may be an icon for presenting the rating link. The execution subject may receive, as the user characteristic information, evaluation information of the target item presented by the user with respect to the item image. The evaluation information may be generated by a user performing an evaluation operation in a page indicated by the evaluation link.
In some optional implementations of this embodiment, the item characteristic information of the target item may include at least one of: the number of browsed times corresponding to the target item, the number of clicked times corresponding to the target item, the number of paid attention corresponding to the target item, the number of times the item information of the target item is added to the target item information set, and the price of the target item. The browsed times corresponding to the target item may include times that an item description page corresponding to the target item is browsed by a user. The clicked times corresponding to the target item may include the clicked times of the page link of the item description page for jumping to the target item by the user. The number of times of interest corresponding to the target item may include the number of times the item identification of the target item is added to the group list or the favorite list by the user. The number of times the item information for the target item is added to the set of target item information may include the number of times the item information for the target item is added to the shopping cart by the user. The price of the target item may include a price of the target item over a preset time period. The preset time period may be one year, six months, etc.
In some optional implementation manners of this embodiment, the executing entity may process the object identifier of the target object based on the target score in the following manner: the execution subject may first determine whether the target score is greater than a preset first score threshold. The first score threshold may be set according to a score range of the target score, and may be, for example, a product of a maximum score in the score range of the target score and a preset first ratio. Here, assuming that the preset first ratio is 0.8, if the target score has a score range of 0 to 1, the first score threshold may be 0.8; if the score range of the target score is 0 to 10, the first score threshold may be 8. If it is determined that the target score is greater than the first score threshold, the execution subject may add the offer information corresponding to the target score to an account associated with the object identifier. The offer information may be full minus information, e.g., full 38 minus 5; the preferential information can also be discount information, for example, 8-fold information for clothing articles; the offer information may also be bonus information, such as information that gifts a specified item at 88 dollars. It should be noted that the execution agent may store a correspondence between the score and the benefit information. The object indicated by the object identification can view the obtained preferential information in the account of the object.
In some optional implementation manners of this embodiment, the executing entity may process the object identifier of the target object based on the target score in the following manner: the execution subject may first determine whether the target score is less than a preset second score threshold. The second score threshold may be set according to a score range of the target score, and may be, for example, a product of a maximum score in the score range of the target score and a preset second ratio. Here, assuming that the preset second ratio is 0.3, if the score range of the target score is 0 to 1, the first score threshold may be 0.3; if the score range of the target score is 0 to 10, the first score threshold may be 3. If it is determined that the target score is smaller than the second score threshold, the execution subject may add the object identifier of the target object to a preset object identifier set. Then, the object identifier set may be pushed to a target terminal, and a user may view the object identifier set on the target terminal to manage the object identifiers in the object identifier set, for example, an object indicated by the object identifier in the object identifier set may be warned, and an object behavior of the object indicated by the object identifier in the object identifier set in a future preset time period may be monitored.
With continued reference to fig. 3, fig. 3 is a schematic diagram of an application scenario of the method for processing information according to the present embodiment. In the application scenario of fig. 3, the server 301 may first obtain relevant feature information 302 for scoring the target object. Here, the related feature information 302 may include: the number of times that the target object is purchased by the user is 1340, the evaluation information of the target object by the user comprises timely response, the performance-price ratio of the recommended object is high, and the price of the object is lower than that of a supermarket. Then, the server 301 may input the relevant feature information 302 into a pre-trained target scoring model 303, and obtain a target score 304 of 91 for the target object. Finally, the server 301 may process the object identification "123" of the target object based on the target score 304. Here, the server 301 determines 91 to be greater than the preset third score threshold 90, and the server 301 may add the object identifier "123" to the set of target object identifiers, so that the target object has a priority for selecting the recommended item.
According to the method provided by the embodiment of the application, the target score of the target object is obtained by inputting the acquired relevant characteristic information for scoring the target object into a pre-trained target scoring model; and then, processing the object identification of the target object based on the target score. By the method, the score of the target object can be determined more accurately, so that the object identification of the target object can be processed more specifically.
With further reference to FIG. 4, a flow 400 of yet another embodiment of a method for processing information is shown. The flow 400 of the method for processing information includes the steps of:
In the present embodiment, an execution subject (e.g., a server shown in fig. 1) of the method for processing information may acquire relevant feature information for scoring a target object. The target object may be a user in the group who makes item recommendation to group members, for example, may be a manager in the group. The related characteristic information may include item characteristic information of the target item and/or user characteristic information of the target user. The target object may include an object recommended by the target object in a group. The item characteristic information of the target item may include, but is not limited to, at least one of: the number of times the target item was purchased by the user and the category to which the target item belongs. The target users may include users in the same group as the target object and/or users who rate the target object. The user characteristic information of the target user may include, but is not limited to, at least one of: the evaluation information of the user on the target object and the activity degree of the user in the group. The user can generate the evaluation information of the article in a star rating or character evaluation mode. Here, the evaluation information may be sent by the user in the group, or may be issued by the user on a preset platform. The above evaluation information may include, but is not limited to, at least one of: the evaluation information of the user on the price of the article, the evaluation information of the user on the quality of the article and the evaluation information of the target object recommending the article. The activity level of the user in the group may include the number of pieces of information sent by the user within a preset time period (for example, 20 minutes) after the target object makes item recommendation in the group.
In this embodiment, the related feature information may include at least one item identifier. The at least one item identifier may include an item identifier of the target item and/or an item identifier of an item evaluated by the target user. If the same item id exists between the item id of the target item and the item id of the item evaluated by the target user, the same item id may be identified as one item id. The at least one item identification belongs to at least one item identification set. Here, each of the at least one item identification sets corresponds to a marketing campaign, such as "limited second kill", "leaderboard", and "good found". Each item identification set in the at least one item identification set corresponds to a pre-trained scoring model. This can cause the data characteristics obtained to be different, as the user's behavior may be different in different marketing campaigns. And for each marketing activity, inputting the data generated by the marketing activity into the scoring model corresponding to the marketing activity, so that a more accurate scoring result can be obtained.
In this embodiment, for each item identifier set in the at least one item identifier set, the executing entity may determine the number of item identifiers belonging to the item identifier set in the at least one item identifier set.
And step 403, determining the pre-trained scoring model corresponding to the maximum number of item identification sets as a target scoring model.
In this embodiment, the executing entity may determine a pre-trained scoring model corresponding to the maximum number of item identifier sets as the target scoring model. As an example, if the number of item identifiers belonging to the first item identifier set is 3, the number of item identifiers belonging to the second item identifier set is 2, and the number of item identifiers belonging to the third item identifier set is 6, the executing entity may determine a pre-trained scoring model corresponding to the third item identifier set as the target scoring model.
And step 404, inputting the relevant characteristic information into a pre-trained target scoring model to obtain a target score of the target object.
In the present embodiment, the steps 404 and 405 can be performed in a similar manner to the steps 202 and 203, and will not be described herein again.
In some optional implementations of this embodiment, if the item identifier set corresponding to the maximum number corresponds to at least two pre-trained scoring models, the executing entity may input the relevant feature information into a pre-trained target scoring model to obtain a target score of the target object by: for each of the determined at least two target scoring models, the executing entity may input the relevant feature information into the target scoring model to obtain an initial score of the target object. Then, the executing body may determine a target score of the target object based on the initial score output by each of the at least two target scoring models. Specifically, when the difference between at least two corresponding pre-trained scoring models is small, the executing entity may average the initial scores output by the plurality of target scoring models, and use the average result as the target score of the target object. When the difference between the at least two corresponding pre-trained scoring models is large, the executing agent may determine a weight of each of the at least two corresponding pre-trained target scoring models based on a correspondence between the item identifier included in the relevant feature information and the pre-trained scoring model, and obtain a weighted average of initial scores obtained by the respective target scoring models as a target score of the target object.
As can be seen from fig. 4, compared with the embodiment corresponding to fig. 2, the flow 400 of the method for processing information in this embodiment embodies the steps of determining the number of item identifiers belonging to each item identifier set in at least one item identifier set, and determining the scoring model corresponding to the item identifier set corresponding to the largest number as the target scoring model. Therefore, the scheme described in this embodiment can determine the target scoring model based on the item identifier in the related feature information, and in this way, the accuracy of the score of the determined target object can be further improved.
With further reference to fig. 5, as an implementation of the methods shown in the above-mentioned figures, the present application provides an embodiment of an apparatus for processing information, which corresponds to the method embodiment shown in fig. 2, and which is particularly applicable in various electronic devices.
As shown in fig. 5, the apparatus 500 for processing information of the present embodiment includes: an acquisition unit 501, an input unit 502, and a processing unit 503. The obtaining unit 501 is configured to obtain relevant feature information for scoring a target object, where the relevant feature information includes item feature information of a target item and/or user feature information of a target user, the target item includes an item recommended by the target object in a group, and the target user includes a user in the same group as the target object and/or a user evaluating the target object; the input unit 502 is configured to input the relevant feature information into a pre-trained target scoring model, resulting in a target score of the target object; the processing unit 503 is configured to process the object identification of the target object based on the target score.
In the present embodiment, specific processing of the acquisition unit 501, the input unit 502, and the processing unit 503 of the apparatus 500 for processing information may refer to step 201, step 202, and step 203 in the corresponding embodiment of fig. 2.
In some alternative implementations of the present embodiment, the apparatus 500 for processing information may include a pushing unit (not shown in the figure) and a receiving unit (not shown in the figure). The push unit may determine whether an item image presented with the target item is received. When the pushing unit receives the item image showing the target item, the evaluation link may be generated based on the item image. Specifically, a page including the item image may be generated, and then an evaluation link corresponding to the page may be generated. After the user clicks the evaluation link at the user terminal, the user terminal may present a page including the item image, and the user may evaluate the target item presented by the item image in the page. Then, the pushing unit may push the evaluation link. In response to detecting the click operation of the user on the preset icon, the pushing unit may push the evaluation link to a user terminal of the user who performs the click operation. The icon may be an icon for presenting the rating link. The receiving unit may receive, as the user characteristic information, evaluation information of the target item presented by the user with respect to the item image. The evaluation information may be generated by a user performing an evaluation operation in a page indicated by the evaluation link.
In some optional implementations of this embodiment, the item characteristic information of the target item may include at least one of: the number of browsed times corresponding to the target item, the number of clicked times corresponding to the target item, the number of paid attention corresponding to the target item, the number of times the item information of the target item is added to the target item information set, and the price of the target item. The browsed times corresponding to the target item may include times that an item description page corresponding to the target item is browsed by a user. The clicked times corresponding to the target item may include the clicked times of the page link of the item description page for jumping to the target item by the user. The number of times of interest corresponding to the target item may include the number of times the item identification of the target item is added to the group list or the favorite list by the user. The number of times the item information for the target item is added to the set of target item information may include the number of times the item information for the target item is added to the shopping cart by the user. The price of the target item may include a price of the target item over a preset time period. The preset time period may be one year, six months, etc.
In some optional implementations of this embodiment, the related feature information may include at least one item identifier. The at least one item identifier may include an item identifier of the target item and/or an item identifier of an item evaluated by the target user. If the same item id exists between the item id of the target item and the item id of the item evaluated by the target user, the same item id may be identified as one item id. The at least one item identification belongs to at least one item identification set. Here, each of the at least one item identification sets corresponds to a marketing campaign, such as "limited second kill", "leaderboard", and "good found". Each item identification set in the at least one item identification set corresponds to a pre-trained scoring model. This can cause the data characteristics obtained to be different, as the user's behavior may be different in different marketing campaigns. And for each marketing activity, inputting the data generated by the marketing activity into the scoring model corresponding to the marketing activity, so that a more accurate scoring result can be obtained.
In some optional implementations of the present embodiment, the apparatus 500 for processing information may further include a first determining unit (not shown in the figure) and a second determining unit (not shown in the figure). For each item identifier set of the at least one item identifier set, the first determining unit may determine the number of item identifiers belonging to the item identifier set in the at least one item identifier set. The second determining unit may determine, as the target scoring model, the pre-trained scoring model corresponding to the maximum number of item identifier sets.
In some optional implementations of this embodiment, if the item identifier sets corresponding to the maximum number correspond to at least two pre-trained scoring models, the input unit 502 may input the relevant feature information into a pre-trained target scoring model to obtain a target score of the target object by: for each of the determined at least two target scoring models, the input unit 502 may input the relevant feature information into the target scoring model to obtain an initial score of the target object. Thereafter, the input unit 502 may determine a target score of the target object based on the initial score output by each of the at least two target scoring models. Specifically, when the difference between at least two corresponding pre-trained scoring models is small, the input unit 502 may average initial scores output by a plurality of target scoring models, and use the average result as the target score of the target object. When the difference between the at least two corresponding pre-trained scoring models is large, the input unit 502 may determine the weight of each of the at least two corresponding pre-trained target scoring models based on the correspondence between the item identifier included in the relevant feature information and the pre-trained scoring model, and calculate a weighted average of the initial scores obtained by the respective target scoring models as the target score of the target object.
In some optional implementations of this embodiment, the processing unit 503 may process the object identifier of the target object based on the target score by: the processing unit 503 may first determine whether the target score is greater than a preset first score threshold. The first score threshold may be set according to a score range of the target score, and may be, for example, a product of a maximum score in the score range of the target score and a preset first ratio. Here, assuming that the preset first ratio is 0.8, if the target score has a score range of 0 to 1, the first score threshold may be 0.8; if the score range of the target score is 0 to 10, the first score threshold may be 8. If it is determined that the target score is greater than the first score threshold, the processing unit 503 may add the offer information corresponding to the target score to the account associated with the object identifier. The offer information may be full minus information, e.g., full 38 minus 5; the preferential information can also be discount information, for example, 8-fold information for clothing articles; the offer information may also be bonus information, such as information that gifts a specified item at 88 dollars. The processing unit 503 may store a correspondence between the score and the benefit information. The object indicated by the object identification can view the obtained preferential information in the account of the object.
In some optional implementations of this embodiment, the processing unit 503 may process the object identifier of the target object based on the target score by: the processing unit 503 may first determine whether the target score is smaller than a preset second score threshold. The second score threshold may be set according to a score range of the target score, and may be, for example, a product of a maximum score in the score range of the target score and a preset second ratio. Here, assuming that the preset second ratio is 0.3, if the score range of the target score is 0 to 1, the first score threshold may be 0.3; if the score range of the target score is 0 to 10, the first score threshold may be 3. If it is determined that the target score is smaller than the second score threshold, the processing unit 503 may add the object identifier of the target object to a preset object identifier set. Then, the object identifier set may be pushed to a target terminal, and a user may view the object identifier set on the target terminal to manage the object identifiers in the object identifier set, for example, an object indicated by the object identifier in the object identifier set may be warned, and an object behavior of the object indicated by the object identifier in the object identifier set in a future preset time period may be monitored.
Referring now to FIG. 6, a schematic diagram of an electronic device (e.g., the server of FIG. 1) 600 suitable for use in implementing embodiments of the present disclosure is shown. The server shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 6, electronic device 600 may include a processing means (e.g., central processing unit, graphics processor, etc.) 601 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage means 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data necessary for the operation of the electronic apparatus 600 are also stored. The processing device 601, the ROM 602, and the RAM603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Generally, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. While fig. 6 illustrates an electronic device 600 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 6 may represent one device or may represent multiple devices as desired.
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 via the communication means 609, or may be installed from the storage means 608, or may be installed from the ROM 602. The computer program, when executed by the processing device 601, performs the above-described functions defined in the methods of embodiments of the present disclosure. It should be noted that the computer readable medium described in the embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium 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 embodiments of the 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 embodiments of the present disclosure, however, a computer readable signal medium may comprise 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: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled 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: acquiring related characteristic information for scoring a target object, wherein the related characteristic information comprises article characteristic information of a target article and/or user characteristic information of a target user, the target article comprises an article recommended by the target object in a group, and the target user comprises a user in the same group with the target object and/or a user for evaluating the target object; inputting the relevant characteristic information into a pre-trained target scoring model to obtain a target score of a target object; and processing the object identification of the target object based on the target score.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
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 and/or flowchart illustration, and combinations of blocks in the block diagrams and/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, an input unit, and a processing unit. The names of the units do not form a limitation to the units themselves in some cases, and for example, the acquiring unit may also be described as a "unit that acquires relevant feature information for scoring a target object".
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (16)
1. A method for processing information, comprising:
acquiring related characteristic information for scoring a target object, wherein the related characteristic information comprises article characteristic information of a target article and/or user characteristic information of a target user, the target article comprises an article recommended by the target object in a group, and the target user comprises a user in the same group with the target object and/or a user evaluating the target object;
inputting the relevant characteristic information into a pre-trained target scoring model to obtain a target score of the target object;
and processing the object identification of the target object based on the target score.
2. The method of claim 1, wherein the method further comprises:
in response to receiving an item image presented with a target item, generating an evaluation link based on the item image, and pushing the evaluation link;
and receiving evaluation information of a target item presented by the user aiming at the item image as user characteristic information, wherein the evaluation information is generated by the user performing evaluation operation in a page indicated by the evaluation link.
3. The method of claim 1, wherein the item characteristic information of the target item comprises at least one of: the number of browsed times corresponding to the target item, the number of clicked times corresponding to the target item, the number of paid attention corresponding to the target item, the number of times the item information of the target item is added to the target item information set, and the price of the target item.
4. The method according to claim 1, wherein the relevant feature information includes at least one item identifier, the at least one item identifier includes an item identifier of the target item and/or an item identifier of an item evaluated by the target user, the at least one item identifier belongs to at least one item identifier set, and each item identifier set in the at least one item identifier set corresponds to a pre-trained scoring model; and
before the inputting the relevant feature information into a pre-trained target scoring model to obtain a target score of the target object, the method further includes:
for each item identification set in the at least one item identification set, determining the number of item identifications belonging to the item identification set in the at least one item identification set;
and determining the pre-trained scoring model corresponding to the maximum number of item identification sets as a target scoring model.
5. The method of claim 4, wherein the set of item identifiers corresponding to a maximum number corresponds to at least two pre-trained scoring models; and
the inputting the relevant feature information into a pre-trained target scoring model to obtain a target score of the target object includes:
for each determined at least two target scoring models, inputting the relevant characteristic information into the target scoring model to obtain an initial score of the target object;
determining a target score of the target object based on the initial scores output by each of the at least two target scoring models.
6. The method of one of claims 1 to 5, wherein said processing the object identification of the target object based on the target score comprises:
in response to determining that the target score is greater than a preset first score threshold, adding preferential information corresponding to the target score to an account associated with the object identifier.
7. The method of one of claims 1 to 5, wherein said processing the object identification of the target object based on the target score comprises:
and in response to determining that the target score is less than a preset second score threshold, adding the object identifier of the target object to a preset object identifier set.
8. An apparatus for processing information, comprising:
the system comprises an acquisition unit, a storage unit and a processing unit, wherein the acquisition unit is configured to acquire related characteristic information for scoring a target object, wherein the related characteristic information comprises article characteristic information of a target article and/or user characteristic information of a target user, the target article comprises articles recommended by the target object in a group, and the target user comprises a user in the same group with the target object and/or a user for evaluating the target object;
an input unit configured to input the relevant feature information into a pre-trained target scoring model, resulting in a target score of the target object;
a processing unit configured to process an object identification of the target object based on the target score.
9. The apparatus of claim 8, wherein the apparatus further comprises:
a pushing unit configured to generate an evaluation link based on an item image presented with a target item in response to receiving the item image, and push the evaluation link;
a receiving unit configured to receive, as user characteristic information, evaluation information of a target item presented by a user with respect to the item image, wherein the evaluation information is generated by the user performing an evaluation operation in a page indicated by the evaluation link.
10. The apparatus of claim 8, wherein the item characteristic information of the target item comprises at least one of: the number of browsed times corresponding to the target item, the number of clicked times corresponding to the target item, the number of paid attention corresponding to the target item, the number of times the item information of the target item is added to the target item information set, and the price of the target item.
11. The apparatus according to claim 8, wherein the relevant feature information includes at least one item identifier, the at least one item identifier includes an item identifier of the target item and/or an item identifier of an item evaluated by the target user, the at least one item identifier belongs to at least one item identifier set, each item identifier set in the at least one item identifier set corresponds to a pre-trained scoring model; and
the device further comprises:
a first determining unit configured to determine, for each of the at least one item identification set, a number of item identifications of the at least one item identification that belong to the item identification set;
and the second determination unit is configured to determine the pre-trained scoring model corresponding to the maximum number of item identification sets as the target scoring model.
12. The apparatus of claim 11, wherein the set of item identifiers corresponding to a maximum number corresponds to at least two pre-trained scoring models; and
the input unit is further configured to input the relevant feature information into a pre-trained target scoring model to obtain a target score of the target object as follows:
for each determined at least two target scoring models, inputting the relevant characteristic information into the target scoring model to obtain an initial score of the target object;
determining a target score of the target object based on the initial scores output by each of the at least two target scoring models.
13. The apparatus according to one of claims 8-12, wherein the processing unit is further configured to process the object identification of the target object based on the target score as follows:
in response to determining that the target score is greater than a preset first score threshold, adding preferential information corresponding to the target score to an account associated with the object identifier.
14. The apparatus according to one of claims 8-12, wherein the processing unit is further configured to process the object identification of the target object based on the target score as follows:
and in response to determining that the target score is less than a preset second score threshold, adding the object identifier of the target object to a preset object identifier set.
15. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
16. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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