CN112307339B - Recommendation information generation method and device based on user portraits and computer equipment - Google Patents
Recommendation information generation method and device based on user portraits and computer equipment Download PDFInfo
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Abstract
The invention discloses a user portrait-based recommendation information generation method, a device, computer equipment and a storage medium, and relates to a big data analysis technology.
Description
Technical Field
The invention relates to the technical field of data analysis of big data, in particular to a recommendation information generation method and device based on user portraits, computer equipment and a storage medium.
Background
At present, in the field of electronic commerce, an automatic generation scheme of content materials is provided, namely after a user sets an activity name, activity time and activity content, the activity name, the activity time and the activity content are uploaded to a server to automatically generate commodity posters or activity posters, and the mode is only to generate the posters with unified content and form and single display form as notification information to push the posters to the user. However, the matching degree (also can be understood as the correlation degree) between the picture and the activity content is not high by the poster generated according to the information such as the activity name, the activity time and the activity content set by the user, which results in lower intuitiveness of the display of the poster.
Disclosure of Invention
The embodiment of the invention provides a recommendation information generation method, device, computer equipment and storage medium based on user portraits, which aim to solve the problem that in the prior art, a poster is generated according to information such as an activity name, an activity time, an activity content and the like set by a user, and the matching degree of pictures and the activity content is not high, so that the intuitiveness of poster display is low.
In a first aspect, an embodiment of the present invention provides a user portrait-based recommendation information generation method, which includes:
if the user selected in the user list is detected, acquiring corresponding target user information;
if the type selected in the recommended information type list is detected, a corresponding recommended information type set is obtained; the recommendation information type set comprises any one or more of pictures, articles and videos;
acquiring a locally stored target user portrait corresponding to the target user information, and acquiring a keyword set corresponding to the target user information according to the target user portrait;
screening and obtaining a corresponding recommended sub-information set in a local content library according to the keyword set and the recommended information type set; the recommended sub information set comprises one or more of pictures, articles and videos;
filling the recommended sub-information set into a called data container to obtain information data to be recommended; and
and if a one-key sending instruction corresponding to the information data to be recommended is detected, sending the information data to be recommended to a user terminal corresponding to the target user information.
In a second aspect, an embodiment of the present invention provides a recommendation information generation apparatus based on a user portrait, including:
the target user information acquisition unit is used for acquiring corresponding target user information if the user selected in the user list is detected;
the recommendation information type acquisition unit is used for acquiring a corresponding recommendation information type set if the type selected in the recommendation information type list is detected; the recommendation information type set comprises any one or more of pictures, articles and videos;
the keyword set acquisition unit is used for acquiring a locally stored target user portrait corresponding to the target user information and acquiring a keyword set corresponding to the target user information according to the target user portrait;
the recommendation sub-information set acquisition unit is used for screening and acquiring a corresponding recommendation sub-information set from a local content library according to the keyword set and the recommendation information type set; the recommended sub information set comprises one or more of pictures, articles and videos;
the information data to be recommended generation unit is used for filling the recommended sub-information set into a called data container so as to obtain information data to be recommended; and
And the recommended data sending unit is used for sending the information data to be recommended to the user side corresponding to the target user information if a one-key sending instruction corresponding to the information data to be recommended is detected.
In a third aspect, an embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor executes the computer program to implement the recommendation information generation method based on user portrait according to the first aspect.
In a fourth aspect, an embodiment of the present invention further provides a computer readable storage medium, where the computer readable storage medium stores a computer program, where the computer program when executed by a processor causes the processor to execute the recommendation information generating method based on a user portrait according to the first aspect.
The embodiment of the invention provides a recommendation information generation method, a device, computer equipment and a storage medium based on user portraits, which are characterized in that firstly, if a user selected in a user list is detected, corresponding target user information is acquired, then, if a type selected in a recommendation information type list is detected, a corresponding recommendation information type set is acquired, a locally stored target user portrait corresponding to the target user information is acquired, a keyword set corresponding to the target user information is acquired according to the target user portraits, a corresponding recommendation sub-information set is obtained by screening in a local content library according to the keyword set and the recommendation information type set, the recommendation sub-information set is filled in a called data container to obtain information data to be recommended, if a one-key sending instruction corresponding to the information data to be recommended is detected, the information data to be recommended is sent to a user end corresponding to the target user information, the content to be recommended is filtered based on the user portraits, and then, the content to be recommended is sent to the user end after being aggregated, and the content to be recommended is more accurately and intuitively checked.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic view of an application scenario of a user portrait-based recommendation information generation method according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method for generating recommendation information based on user portraits according to an embodiment of the present invention;
FIG. 3 is a schematic block diagram of a user portrait-based recommendation information generation apparatus according to an embodiment of the present invention;
fig. 4 is a schematic block diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be understood that the terms "comprises" and "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
Referring to fig. 1 and fig. 2, fig. 1 is a schematic view of an application scenario of a user portrait-based recommendation information generation method according to an embodiment of the present invention; fig. 2 is a flowchart of a user portrait based recommendation information generation method according to an embodiment of the present invention, where the user portrait based recommendation information generation method is applied to a server, and the method is executed by application software installed in the server.
As shown in fig. 2, the method includes steps S110 to S160.
S110, if the user selected in the user list is detected, corresponding target user information is acquired.
In this embodiment, in order to recommend information to other users, when the user a logs in to a corresponding information recommendation system in the server through its user account, a user is selected (possibly only one user is selected or a plurality of users are selected) in a user list displayed on a UI interface (i.e. a user interaction interface) of the information recommendation system, and the selected user is taken as a target user to obtain target user information corresponding to each target user. The target user information comprises a user name, a user mobile phone number and the like. And selecting the target user in the user list, and then, the target user is the target user needing to send the recommendation information.
More specifically, when the number of selected users in the user list is plural, the user a may further group the selected plural target users into one or more user groups, so that information may be pushed specifically for each user group.
S120, if the type selected in the recommended information type list is detected, a corresponding recommended information type set is obtained; wherein the recommended information type set comprises any one or more of pictures, articles and videos.
In this embodiment, after the user a completes the selection of the target user on the UI interface of the information recommendation system, the user a needs to select a recommendation information type, for example, any one or more of several types including a picture, an article, and a video may be selected, so that content data may be recommended to the target user according to the selected recommendation information type in the information recommendation system of the server.
S130, acquiring a target user portrait corresponding to the target user information, which is stored locally, and acquiring a keyword set corresponding to the target user information according to the target user portrait.
In this embodiment, in order to push content data to a target user more accurately, a target user portrait corresponding to the target user information may be acquired in a user database stored locally in a server.
Because the target user information comprises the user name and the user mobile phone number, the unique user and the corresponding target user portrait can be determined according to the target user information. The target user portraits are high-correlation user labels extracted based on practical application scenes (such as user portraits obtained for consumers of the product A), and the user labels can be understood as a plurality of keywords, so that a corresponding keyword set consisting of the user labels can be obtained according to the user portraits.
In one embodiment, step S130 includes:
and acquiring an initial keyword set included in the target user portrait, and screening tag values respectively corresponding to a gender tag, an age tag, a region tag, a income tag and a user behavior tag from the initial keyword set to form a keyword set corresponding to the target user information.
In this embodiment, since the target user portrait includes more keywords corresponding to the user tags, it is not necessary that each user tag is a core keyword required by the user a, and at this time, a core tag screening policy pre-configured in the server may be invoked to implement screening of tag values corresponding to the gender tag, the age tag, the region tag, the income tag and the user behavior tag from the initial keyword set, and a keyword set is formed by the tag value sets. For example, the key word set is composed of 5 tag values of men, middle-aged, shenzhen, medium income and attention to international popular news.
S140, screening and obtaining a corresponding recommended sub-information set from a local content library according to the keyword set and the recommended information type set; the recommended sub information set comprises one or more of pictures, articles and videos.
In this embodiment, after the keyword set corresponding to the target user information is obtained, since the recommendation information type set is also set before, the corresponding recommendation sub-information set may be obtained by screening in the local content library of the server according to the keyword set and the recommendation information type set.
For example, the local content library (which can be understood as a data center) in the server can be divided into 3 large types of content libraries: firstly, a picture library; secondly, an article library; thirdly, a video library. And if the type of the recommended information set by the user A comprises pictures and articles, screening and acquiring corresponding recommended sub-information in a picture library and an article library respectively by using the keyword set, so as to form a recommended sub-information set.
The number of the information types included in the recommended sub-information set is the same as the number of the recommended information types in the recommended information type set, and the information types included in the recommended sub-information set are the same as the recommended information types in the recommended information type set.
For example, a keyword set is formed by 5 tag values of incomes of men, middle-aged, shenzhen and medium-grade and attention to international popular news, and the recommendation information type set by the user a includes pictures and articles, and 5 user tags of incomes of men, middle-aged, shenzhen and medium-grade and attention to international popular news are taken as keywords to search a target picture in a picture library (the target pictures are all tagged, and if a certain target picture has the same tag as 5 user tags of men, middle-aged, shenzhen, medium-grade and attention to international popular news), an initial screening picture recommendation set is formed by the target picture. Similarly, 5 user tags of male, middle-aged, shenzhen, and medium-sized income and international trending news are taken as keywords to search for target articles in an article library (all the target articles are labeled, and if a certain target article has the same label as 5 user tags of male, middle-aged, shenzhen, and medium-sized income and international trending news), an initial screening article recommendation set is formed by the target articles. Through the content screening based on the user portrait, recommended content can be more accurate.
In one embodiment, step S140 includes:
taking each keyword and recommendation information type set in the keyword set as screening conditions, and acquiring an initial screening picture recommendation set, an initial screening article recommendation set and an initial screening video recommendation set from a local content library;
obtaining the screening pictures corresponding to the initial screening picture recommendation set, wherein the picture click magnitude ranking of the screening pictures does not exceed a preset ranking threshold value, so as to form a recommendation picture set;
obtaining the screened articles corresponding to the article reading value ranking in the initial screened article recommendation set, which does not exceed a preset ranking threshold value, so as to form a recommendation article set;
acquiring the screening videos corresponding to the initial screening video recommendation set, wherein the video play magnitude ranking of the screening videos does not exceed a preset ranking threshold value, so as to form a recommendation video set;
and forming a recommendation sub-information set by the recommendation picture set, the recommendation article set and the recommendation video set.
In this embodiment, in order to retrieve a picture set, an article set and a video set more quickly and accurately, an initial screening picture recommendation set, an initial screening article recommendation set and an initial screening video recommendation set may be obtained in a local content library by using each keyword and recommendation information type set in the keyword set as screening conditions. For example, if the set recommendation information type set includes pictures and articles, the initial filter picture recommendation set, the initial filter article recommendation set are not empty sets, and the initial filter video recommendation set is empty set.
In order to recommend pictures, articles and videos with higher attention and event hotspots to target users, screening pictures corresponding to the initial screening picture recommendation set with the picture click magnitude rank not exceeding the ranking threshold (the ranking threshold can be set to 3) can be obtained, screening articles corresponding to the initial screening article recommendation set with the article reading magnitude rank not exceeding the ranking threshold can be obtained, screening videos corresponding to the initial screening video recommendation set with the video play magnitude rank not exceeding the ranking threshold can be obtained, and a recommendation sub-information set is formed by screening the selected recommendation picture set, recommendation article set and recommendation video set. In this way, the number of recommended content is reduced, and inconvenience in view of users caused by too many stacks of recommended content is avoided.
In an embodiment, step S140 further includes:
and screening and obtaining a corresponding recommended product information set from a local product information base according to the keyword set.
In this embodiment, since the recommended sub-information set is a popular picture, article and/or video selected from the local content library, the recommended product information that the user a wants to push to other users may not be included, and at this time, the corresponding recommended product information set may also be selected and obtained from the local product information library based on the user portrait, so that the recommended sub-information set is pushed to other users together with the recommended sub-information set.
For example, the 4 user tags of male, middle-aged, shenzhen and medium income are used as keywords to search the corresponding recommended product information set in the local product information base, more specifically, if the plurality of products respectively correspond to the 4 user tags of male, middle-aged, shenzhen and medium income, the product with the front 5 of the product sales can be selected from the plurality of product lists, and the recommended product information set is composed by the 5 corresponding recommended product information of the products, so that the recommended product information set is pushed to other users together with the recommended sub-information set.
And S150, filling the recommended sub-information set into a called data container to obtain information data to be recommended.
In this embodiment, since the recommended sub-information sets were previously filtered based on the user portrait, if the recommended sub-information sets are pushed to the user side with scattered contents, the user is not convenient to view the recommended sub-information sets. In order to gather the recommended sub-information sets in a concentrated area, the recommended sub-information sets can be filled into a called data container, and the recommended sub-information sets are loaded by taking the data container as a carrier. When the data container loaded with the content data is sent to the user side, the user can open the data container to intensively check the data pushed by the server at this time.
In one embodiment, step S150 includes:
creating a blank card container with empty data in advance;
acquiring the total number of the recommendation sub-information included in the recommendation sub-information set;
creating a sub-card area with the same number as the total number in the blank card container;
filling a recommendation sub-information in each sub-card area to obtain a current card;
and each sub-card area in the current card is correspondingly and automatically added with a buried point to obtain information data to be recommended.
In this embodiment, for better aggregation of content data, a card container may be selected as a carrier to load the recommended sub-information sets. A card, which can be understood as a component on a UI interface provided by a server, can be used as a container to conveniently display contents composed of different data elements (such as pictures, articles, and videos). Firstly creating a blank card container, then counting the total number of recommended sub-information included in the recommended sub-information set, determining how many sub-card areas the blank card container is to be divided into, and finally filling one recommended sub-information in each sub-card area to obtain the current card.
In order to increase the data acquisition function, each sub-card area in the current card is also required to be correspondingly and automatically added with buried points to obtain information data to be recommended. Thus, when the user side receiving the card type information data to be recommended clicks one sub-card area, the behavior data generated by clicking one sub-card area is transmitted back to the storage area which is divided corresponding to the information data to be recommended in the server. In this way, each time information data to be recommended is pushed to the user side in the server, behavior data generated in the user side is returned, and a closed-loop data processing process is formed.
In a specific implementation, a sub-card area can be created in the blank card container for accommodating the recommended product information set, so that a composite function card for content recommendation and product recommendation is formed, and a user can acquire more data content.
In an embodiment, step S150 further includes:
judging whether a sub-card area adjustment instruction is detected;
if a sub-card area adjustment instruction is detected and is a sub-card area content deletion instruction, deleting recommended sub-information in the corresponding sub-card area to update information data to be recommended;
if the sub-card area adjusting instruction is detected and is a sub-card area content replacing instruction, replacing the recommended sub-information in the corresponding sub-card area according to the replacing information so as to update the information data to be recommended.
In this embodiment, in the current card corresponding to the information data to be recommended, the contents in each sub-card area may be replaced, deleted, etc. according to the editing requirement, so as to implement free editing on the current card.
And S160, if a one-key sending instruction corresponding to the information data to be recommended is detected, the information data to be recommended is sent to a user side corresponding to the target user information.
In this embodiment, after the editing of the current card is completed and the information data to be recommended is finally obtained, if a one-key sending instruction triggered by the user a on the UI interface of the information recommendation system is detected, the information data to be recommended in the form of a card is sent to the user side corresponding to the target user information by the server. In this way, other users corresponding to the user side receiving the information data to be recommended can click on the information data to be recommended in the form of a card to view the content.
In an embodiment, step S160 further includes:
acquiring acquisition data sent by each buried point in the information data to be recommended so as to form a feedback data set; the acquisition data sent by each embedded point comprises browsing quantity, forwarding quantity, praise times, message content and stay time;
invoking a preset label conversion strategy to convert the data items in the feedback data set into corresponding user behavior labels;
and updating the target user portrait by the user behavior tag to obtain an updated user portrait.
In this embodiment, in order to feed back the user behavior data (i.e., the browsing amount, the forwarding amount, the praise number, the message content and the stay time) collected by the buried point to the server, the server may convert the data items in the feedback data set into corresponding user behavior tags according to the user behavior data and a preset tag conversion policy, and fuse the user behavior tags as new tags to the target user portrait for updating, so as to obtain an updated user portrait, thereby forming a closed-loop data processing process.
The method realizes that the content to be recommended is screened based on the user portrait, the content to be recommended is aggregated and then sent to the user side, the content classification is more accurate, and the user is more convenient to view in a centralized visual mode.
The embodiment of the invention also provides a recommendation information generation device based on the user portrait, which is used for executing any embodiment of the recommendation information generation method based on the user portrait. Specifically, referring to fig. 3, fig. 3 is a schematic block diagram of a recommendation information generation device based on user portraits according to an embodiment of the present invention. The user profile-based recommendation information generation apparatus 100 may be configured in a server.
As shown in fig. 3, the recommendation information generation device 100 based on a user portrait includes: the target user information acquisition unit 110, the recommended information type acquisition unit 120, the keyword set acquisition unit 130, the keyword set acquisition unit 140, the information data to be recommended generation unit 150, and the recommended data transmission unit 160.
The target user information obtaining unit 110 is configured to obtain corresponding target user information if a user selected in the user list is detected.
In this embodiment, in order to recommend information to other users, when the user a logs in to a corresponding information recommendation system in the server through its user account, a user is selected (possibly only one user is selected or a plurality of users are selected) in a user list displayed on a UI interface (i.e. a user interaction interface) of the information recommendation system, and the selected user is taken as a target user to obtain target user information corresponding to each target user. The target user information comprises a user name, a user mobile phone number and the like. And selecting the target user in the user list, and then, the target user is the target user needing to send the recommendation information.
More specifically, when the number of selected users in the user list is plural, the user a may further group the selected plural target users into one or more user groups, so that information may be pushed specifically for each user group.
A recommended information type obtaining unit 120, configured to obtain a corresponding recommended information type set if a type selected in the recommended information type list is detected; wherein the recommended information type set comprises any one or more of pictures, articles and videos.
In this embodiment, after the user a completes the selection of the target user on the UI interface of the information recommendation system, the user a needs to select a recommendation information type, for example, any one or more of several types including a picture, an article, and a video may be selected, so that content data may be recommended to the target user according to the selected recommendation information type in the information recommendation system of the server.
And a keyword set acquisition unit 130, configured to acquire a locally stored target user portrait corresponding to the target user information, and acquire a keyword set corresponding to the target user information according to the target user portrait.
In this embodiment, in order to push content data to a target user more accurately, a target user portrait corresponding to the target user information may be acquired in a user database stored locally in a server.
Because the target user information comprises the user name and the user mobile phone number, the unique user and the corresponding target user portrait can be determined according to the target user information. The target user portraits are high-correlation user labels extracted based on practical application scenes (such as user portraits obtained for consumers of the product A), and the user labels can be understood as a plurality of keywords, so that a corresponding keyword set consisting of the user labels can be obtained according to the user portraits.
In an embodiment, the keyword set acquisition unit 130 is further configured to:
and acquiring an initial keyword set included in the target user portrait, and screening tag values respectively corresponding to a gender tag, an age tag, a region tag, a income tag and a user behavior tag from the initial keyword set to form a keyword set corresponding to the target user information.
In this embodiment, since the target user portrait includes more keywords corresponding to the user tags, it is not necessary that each user tag is a core keyword required by the user a, and at this time, a core tag screening policy pre-configured in the server may be invoked to implement screening of tag values corresponding to the gender tag, the age tag, the region tag, the income tag and the user behavior tag from the initial keyword set, and a keyword set is formed by the tag value sets. For example, the key word set is composed of 5 tag values of men, middle-aged, shenzhen, medium income and attention to international popular news.
A keyword set obtaining unit 140, configured to screen and obtain a corresponding recommended sub-information set from a local content library according to the keyword set and the recommended information type set; the recommended sub information set comprises one or more of pictures, articles and videos.
In this embodiment, after the keyword set corresponding to the target user information is obtained, since the recommendation information type set is also set before, the corresponding recommendation sub-information set may be obtained by screening in the local content library of the server according to the keyword set and the recommendation information type set.
For example, the local content library (which can be understood as a data center) in the server can be divided into 3 large types of content libraries: firstly, a picture library; secondly, an article library; thirdly, a video library. And if the type of the recommended information set by the user A comprises pictures and articles, screening and acquiring corresponding recommended sub-information in a picture library and an article library respectively by using the keyword set, so as to form a recommended sub-information set.
The number of the information types included in the recommended sub-information set is the same as the number of the recommended information types in the recommended information type set, and the information types included in the recommended sub-information set are the same as the recommended information types in the recommended information type set.
For example, a keyword set is formed by 5 tag values of incomes of men, middle-aged, shenzhen and medium-grade and attention to international popular news, and the recommendation information type set by the user a includes pictures and articles, and 5 user tags of incomes of men, middle-aged, shenzhen and medium-grade and attention to international popular news are taken as keywords to search a target picture in a picture library (the target pictures are all tagged, and if a certain target picture has the same tag as 5 user tags of men, middle-aged, shenzhen, medium-grade and attention to international popular news), an initial screening picture recommendation set is formed by the target picture. Similarly, 5 user tags of male, middle-aged, shenzhen, and medium-sized income and international trending news are taken as keywords to search for target articles in an article library (all the target articles are labeled, and if a certain target article has the same label as 5 user tags of male, middle-aged, shenzhen, and medium-sized income and international trending news), an initial screening article recommendation set is formed by the target articles. Through the content screening based on the user portrait, recommended content can be more accurate.
In an embodiment, the keyword set acquisition unit 140 includes:
The primary screening unit is used for taking each keyword and recommendation information type set in the keyword set as screening conditions and acquiring an initial screening picture recommendation set, an initial screening article recommendation set and an initial screening video recommendation set from a local content library;
the recommended picture set acquisition unit is used for acquiring the screened pictures corresponding to the initial screened picture recommended set, wherein the picture click magnitude rank of the screened pictures does not exceed a preset ranking threshold value, so as to form a recommended picture set;
the article recommendation set acquisition unit is used for acquiring the screened articles corresponding to the article reading value ranking in the initial screened article recommendation set, which does not exceed the preset ranking threshold value, so as to form a recommended article set;
the recommended video set acquisition unit is used for acquiring the screened videos corresponding to the initial screened video recommended set with the video play magnitude ranking not exceeding a preset ranking threshold value so as to form a recommended video set;
and the recommendation set combining unit is used for forming a recommendation sub-information set by the recommendation picture set, the recommendation article set and the recommendation video set.
In this embodiment, in order to retrieve a picture set, an article set and a video set more quickly and accurately, an initial screening picture recommendation set, an initial screening article recommendation set and an initial screening video recommendation set may be obtained in a local content library by using each keyword and recommendation information type set in the keyword set as screening conditions. For example, if the set recommendation information type set includes pictures and articles, the initial filter picture recommendation set, the initial filter article recommendation set are not empty sets, and the initial filter video recommendation set is empty set.
In order to recommend pictures, articles and videos with higher attention and event hotspots to target users, screening pictures corresponding to the initial screening picture recommendation set with the picture click magnitude rank not exceeding the ranking threshold (the ranking threshold can be set to 3) can be obtained, screening articles corresponding to the initial screening article recommendation set with the article reading magnitude rank not exceeding the ranking threshold can be obtained, screening videos corresponding to the initial screening video recommendation set with the video play magnitude rank not exceeding the ranking threshold can be obtained, and a recommendation sub-information set is formed by screening the selected recommendation picture set, recommendation article set and recommendation video set. In this way, the number of recommended content is reduced, and inconvenience in view of users caused by too many stacks of recommended content is avoided.
In one embodiment, the recommendation information generation apparatus 100 based on the user portraits further includes:
and the recommended product information set acquisition unit is used for screening and acquiring a corresponding recommended product information set from the local product information base according to the keyword set.
In this embodiment, since the recommended sub-information set is a popular picture, article and/or video selected from the local content library, the recommended product information that the user a wants to push to other users may not be included, and at this time, the corresponding recommended product information set may also be selected and obtained from the local product information library based on the user portrait, so that the recommended sub-information set is pushed to other users together with the recommended sub-information set.
For example, the 4 user tags of male, middle-aged, shenzhen and medium income are used as keywords to search the corresponding recommended product information set in the local product information base, more specifically, if the plurality of products respectively correspond to the 4 user tags of male, middle-aged, shenzhen and medium income, the product with the front 5 of the product sales can be selected from the plurality of product lists, and the recommended product information set is composed by the 5 corresponding recommended product information of the products, so that the recommended product information set is pushed to other users together with the recommended sub-information set.
And the information data to be recommended generating unit 150 is configured to fill the recommended sub-information set into a called data container to obtain information data to be recommended.
In this embodiment, since the recommended sub-information sets were previously filtered based on the user portrait, if the recommended sub-information sets are pushed to the user side with scattered contents, the user is not convenient to view the recommended sub-information sets. In order to gather the recommended sub-information sets in a concentrated area, the recommended sub-information sets can be filled into a called data container, and the recommended sub-information sets are loaded by taking the data container as a carrier. When the data container loaded with the content data is sent to the user side, the user can open the data container to intensively check the data pushed by the server at this time.
In an embodiment, the information data to be recommended generating unit 150 includes:
a blank card container creation unit for creating a blank card container whose data is empty in advance;
a recommendation sub-information number statistics unit, configured to obtain a total number of recommendation sub-information included in the recommendation sub-information set;
a self-card area creation unit configured to create sub-card areas having the same number as the total number in the blank card container;
the current card acquisition unit is used for filling one recommended sub-information in each sub-card area to obtain a current card;
and the embedded point adding unit is used for automatically adding embedded points to each sub-card area in the current card to obtain information data to be recommended.
In this embodiment, for better aggregation of content data, a card container may be selected as a carrier to load the recommended sub-information sets. A card, which can be understood as a component on a UI interface provided by a server, can be used as a container to conveniently display contents composed of different data elements (such as pictures, articles, and videos). Firstly creating a blank card container, then counting the total number of recommended sub-information included in the recommended sub-information set, determining how many sub-card areas the blank card container is to be divided into, and finally filling one recommended sub-information in each sub-card area to obtain the current card.
In order to increase the data acquisition function, each sub-card area in the current card is also required to be correspondingly and automatically added with buried points to obtain information data to be recommended. Thus, when the user side receiving the card type information data to be recommended clicks one sub-card area, the behavior data generated by clicking one sub-card area is transmitted back to the storage area which is divided corresponding to the information data to be recommended in the server. In this way, each time information data to be recommended is pushed to the user side in the server, behavior data generated in the user side is returned, and a closed-loop data processing process is formed.
In a specific implementation, a sub-card area can be created in the blank card container for accommodating the recommended product information set, so that a composite function card for content recommendation and product recommendation is formed, and a user can acquire more data content.
In one embodiment, the recommendation information generation apparatus 100 based on the user portraits further includes:
the area adjusting unit is used for judging whether a sub-card area adjusting instruction is detected;
the content deleting unit is used for deleting the recommended sub-information in the corresponding sub-card area to update the information data to be recommended if the sub-card area adjusting instruction is detected and is a sub-card area content deleting instruction;
And the content replacement unit is used for replacing the recommended sub-information in the corresponding sub-card area according to the replacement information so as to update the information data to be recommended if the sub-card area adjustment instruction is detected and is a sub-card area content replacement instruction.
In this embodiment, in the current card corresponding to the information data to be recommended, the contents in each sub-card area may be replaced, deleted, etc. according to the editing requirement, so as to implement free editing on the current card.
And the recommended data sending unit 160 is configured to send the information data to be recommended to a user terminal corresponding to the target user information if a one-key sending instruction corresponding to the information data to be recommended is detected.
In this embodiment, after the editing of the current card is completed and the information data to be recommended is finally obtained, if a one-key sending instruction triggered by the user a on the UI interface of the information recommendation system is detected, the information data to be recommended in the form of a card is sent to the user side corresponding to the target user information by the server. In this way, other users corresponding to the user side receiving the information data to be recommended can click on the information data to be recommended in the form of a card to view the content.
In one embodiment, the recommendation information generation apparatus 100 based on the user portraits further includes:
the feedback data set acquisition unit is used for acquiring acquisition data sent by each buried point in the information data to be recommended so as to form a feedback data set; the acquisition data sent by each embedded point comprises browsing quantity, forwarding quantity, praise times, message content and stay time;
the user behavior label unit is used for calling a preset label conversion strategy and converting the data items in the feedback data set into corresponding user behavior labels;
and the user portrait updating unit is used for updating the target user portrait through the user behavior tag to obtain an updated user portrait.
In this embodiment, in order to feed back the user behavior data (i.e., the browsing amount, the forwarding amount, the praise number, the message content and the stay time) collected by the buried point to the server, the server may convert the data items in the feedback data set into corresponding user behavior tags according to the user behavior data and a preset tag conversion policy, and fuse the user behavior tags as new tags to the target user portrait for updating, so as to obtain an updated user portrait, thereby forming a closed-loop data processing process.
The device realizes that the content to be recommended is filtered based on the user portrait, the content to be recommended is sent to the user side after being aggregated, the content classification is more accurate, and the user is more convenient to concentrate on visual viewing. The application can also be applied to intelligent education scenes, thereby promoting the construction of intelligent cities.
The user portrayal-based recommendation information generating means described above may be implemented in the form of a computer program which may be run on a computer device as shown in fig. 4.
Referring to fig. 4, fig. 4 is a schematic block diagram of a computer device according to an embodiment of the present application. The computer device 500 is a server, and the server may be a stand-alone server or a server cluster formed by a plurality of servers.
With reference to FIG. 4, the computer device 500 includes a processor 502, memory, and a network interface 505, connected by a system bus 501, where the memory may include a non-volatile storage medium 503 and an internal memory 504.
The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032, when executed, causes the processor 502 to perform a recommendation information generation method based on a user representation.
The processor 502 is used to provide computing and control capabilities to support the operation of the overall computer device 500.
The internal memory 504 provides an environment for the execution of a computer program 5032 in the non-volatile storage medium 503, which computer program 5032, when executed by the processor 502, causes the processor 502 to execute a recommendation information generation method based on a user portrait.
The network interface 505 is used for network communication, such as providing for transmission of data information, etc. It will be appreciated by those skilled in the art that the architecture shown in fig. 4 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting of the computer device 500 to which the present inventive arrangements may be implemented, and that a particular computer device 500 may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
The processor 502 is configured to execute a computer program 5032 stored in a memory, so as to implement the user portrait based recommendation information generation method disclosed in the embodiment of the present invention.
Those skilled in the art will appreciate that the embodiment of the computer device shown in fig. 4 is not limiting of the specific construction of the computer device, and in other embodiments, the computer device may include more or less components than those shown, or certain components may be combined, or a different arrangement of components. For example, in some embodiments, the computer device may include only a memory and a processor, and in such embodiments, the structure and function of the memory and the processor are consistent with the embodiment shown in fig. 4, and will not be described again.
It should be appreciated that in embodiments of the present invention, the processor 502 may be a central processing unit (Central Processing Unit, CPU), the processor 502 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf Programmable gate arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
In another embodiment of the invention, a computer-readable storage medium is provided. The computer readable storage medium may be a non-volatile computer readable storage medium. The computer readable storage medium stores a computer program, wherein the computer program when executed by a processor implements the user portrait based recommendation information generation method disclosed in the embodiment of the present invention.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the apparatus, device and unit described above may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein. Those of ordinary skill in the art will appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein may be embodied in electronic hardware, in computer software, or in a combination of the two, and that the elements and steps of the examples have been generally described in terms of function in the foregoing description to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the units is merely a logical function division, there may be another division manner in actual implementation, or units having the same function may be integrated into one unit, for example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices, or elements, or may be an electrical, mechanical, or other form of connection.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment of the present invention.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units may be stored in a storage medium if implemented in the form of software functional units and sold or used as stand-alone products. Based on such understanding, the technical solution of the present invention is essentially or a part contributing to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, an optical disk, or other various media capable of storing program codes.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (7)
1. A recommendation information generation method based on a user portrait, comprising:
if the user selected in the user list is detected, acquiring corresponding target user information;
if the type selected in the recommended information type list is detected, a corresponding recommended information type set is obtained; the recommendation information type set comprises any one or more of pictures, articles and videos;
acquiring a locally stored target user portrait corresponding to the target user information, and acquiring a keyword set corresponding to the target user information according to the target user portrait;
screening and obtaining a corresponding recommended sub-information set in a local content library according to the keyword set and the recommended information type set; the recommended sub information set comprises one or more of pictures, articles and videos;
Filling the recommended sub-information set into a called data container to obtain information data to be recommended; and
if a one-key sending instruction corresponding to the information data to be recommended is detected, the information data to be recommended is sent to a user side corresponding to the target user information;
the step of obtaining the keyword set corresponding to the target user information according to the target user portrait comprises the following steps:
acquiring an initial keyword set included in the target user portrait, and screening tag values respectively corresponding to a gender tag, an age tag, a region tag, a income tag and a user behavior tag from the initial keyword set to form a keyword set corresponding to the target user information;
the step of screening the corresponding recommended sub-information set in the local content library according to the keyword set and the recommended information type set, including:
taking each keyword and recommendation information type set in the keyword set as screening conditions, and acquiring an initial screening picture recommendation set, an initial screening article recommendation set and an initial screening video recommendation set from a local content library;
obtaining the screening pictures corresponding to the initial screening picture recommendation set, wherein the picture click magnitude ranking of the screening pictures does not exceed a preset ranking threshold value, so as to form a recommendation picture set;
Obtaining the screened articles corresponding to the article reading value ranking in the initial screened article recommendation set, which does not exceed a preset ranking threshold value, so as to form a recommendation article set;
acquiring the screening videos corresponding to the initial screening video recommendation set, wherein the video play magnitude ranking of the screening videos does not exceed a preset ranking threshold value, so as to form a recommendation video set;
a recommendation sub-information set is formed by the recommendation picture set, the recommendation article set and the recommendation video set;
the filling the recommended sub information set into a called data container to obtain information data to be recommended, including:
creating a blank card container with empty data in advance;
acquiring the total number of the recommendation sub-information included in the recommendation sub-information set;
creating a sub-card area with the same number as the total number in the blank card container;
filling a recommendation sub-information in each sub-card area to obtain a current card;
and each sub-card area in the current card is correspondingly and automatically added with a buried point to obtain information data to be recommended.
2. The user portrait based recommendation information generation method according to claim 1, further comprising:
and screening and obtaining a corresponding recommended product information set from a local product information base according to the keyword set.
3. The method for generating user portrait based recommendation information according to claim 1, wherein after detecting a one-touch transmission instruction corresponding to the information data to be recommended, transmitting the information data to be recommended to a user terminal corresponding to the target user information, further comprises:
acquiring acquisition data sent by each buried point in the information data to be recommended so as to form a feedback data set; the acquisition data sent by each embedded point comprises browsing quantity, forwarding quantity, praise times, message content and stay time;
invoking a preset label conversion strategy to convert the data items in the feedback data set into corresponding user behavior labels;
and updating the target user portrait by the user behavior tag to obtain an updated user portrait.
4. The method for generating recommendation information based on user portraits according to claim 1, wherein after filling the recommendation sub-information sets into the invoked data containers to obtain the information data to be recommended, further comprising:
judging whether a sub-card area adjustment instruction is detected;
if a sub-card area adjustment instruction is detected and is a sub-card area content deletion instruction, deleting recommended sub-information in the corresponding sub-card area to update information data to be recommended;
If the sub-card area adjusting instruction is detected and is a sub-card area content replacing instruction, replacing the recommended sub-information in the corresponding sub-card area according to the replacing information so as to update the information data to be recommended.
5. A user portrayal-based recommendation information generating device for implementing the method according to any one of claims 1 to 4, characterized in that the device comprises:
the target user information acquisition unit is used for acquiring corresponding target user information if the user selected in the user list is detected;
the recommendation information type acquisition unit is used for acquiring a corresponding recommendation information type set if the type selected in the recommendation information type list is detected; the recommendation information type set comprises any one or more of pictures, articles and videos;
the keyword set acquisition unit is used for acquiring a locally stored target user portrait corresponding to the target user information and acquiring a keyword set corresponding to the target user information according to the target user portrait;
the recommendation sub-information set acquisition unit is used for screening and acquiring a corresponding recommendation sub-information set from a local content library according to the keyword set and the recommendation information type set; the recommended sub information set comprises one or more of pictures, articles and videos;
The information data to be recommended generation unit is used for filling the recommended sub-information set into a called data container so as to obtain information data to be recommended; and
and the recommended data sending unit is used for sending the information data to be recommended to the user side corresponding to the target user information if a one-key sending instruction corresponding to the information data to be recommended is detected.
6. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the user portrayal-based recommendation information generation method according to any one of claims 1 to 4 when executing the computer program.
7. A computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the user portrayal-based recommendation information generating method according to any one of claims 1 to 4.
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| CN113420209A (en) * | 2021-06-21 | 2021-09-21 | 北京搜狗科技发展有限公司 | Recommendation method, device and equipment based on weather search and storage medium |
| CN113792213A (en) * | 2021-09-07 | 2021-12-14 | 东风汽车有限公司东风日产乘用车公司 | Service recommendation method, device, equipment and storage medium |
| CN113821728B (en) * | 2021-09-29 | 2023-08-25 | 平安银行股份有限公司 | Content Recommendation Method and Device |
| CN114662007B (en) * | 2022-05-25 | 2022-09-02 | 太平金融科技服务(上海)有限公司深圳分公司 | Data recommendation method and device, computer equipment and storage medium |
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