CN112836127B - Method and device for recommending social users, storage medium and electronic equipment - Google Patents
Method and device for recommending social users, storage medium and electronic equipment Download PDFInfo
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Abstract
The embodiment of the application discloses a method for recommending social users, which comprises the following steps: removing hot users in the first attention list of the target user to obtain a second attention list; acquiring a user list to be recommended from a user database of the social platform based on the second attention list; calculating the recommendation weight of each user to be recommended in the user list to be recommended based on the user list to be recommended; and acquiring a recommended user in the at least one user to be recommended based on the recommended weight and a preset rule, and recommending the recommended user to the target user. By adopting the embodiment of the application, personalized recommendation aiming at the target user can be realized, and the recommendation effect of the network social user recommendation is obviously improved.
Description
Technical Field
The present disclosure relates to the field of internet data mining technologies, and in particular, to a method, an apparatus, a storage medium, and an electronic device for recommending social users.
Background
With the advent of the "internet+" age, the internet ecosystem has evolved, and as the most active and important component of this ecosystem, netizens have all affected this ecosphere in one action. At present, internet requirements of netizens are not only to review data and browse information, but more and more netizens conduct shopping, social contact, entertainment and other activities through the internet. The online social connection is a part of internet life of netizens, such as popularity of a social platform of microblog, tremble and the like, provides relatively open information real-time sharing places for netizens, and provides a friend making way for the netizens by a user attention mechanism of the social platform, and meets information sharing and social demands of the netizens. However, in the prior art, when a user wants to acquire more users with the same interests as the user to perform friendly social contact on an open social platform, the user can only see the user who is not interested in the user because the recommendation algorithm of the social platform is behind or the recommendation algorithm is not suitable for the social platform.
Disclosure of Invention
The embodiment of the application provides a social user recommending method, a social user recommending device, a storage medium and electronic equipment, personalized recommendation aiming at a target user can be realized, and the recommending effect of network social user recommendation is obviously improved. The technical scheme is as follows:
in a first aspect, an embodiment of the present application provides a method for recommending social users, including:
removing hot users in the first attention list of the target user to obtain a second attention list; the hot spot users are users with the interaction number exceeding an interaction threshold value and/or the attention number exceeding an attention threshold value in the social platform;
acquiring a user list to be recommended from a user database of the social platform based on the second attention list; the user to be recommended list comprises at least one user to be recommended, and the attention list of the user to be recommended comprises at least one user in the second attention list;
calculating the recommendation weight of each user to be recommended in the user list to be recommended based on the user list to be recommended;
and acquiring a recommended user in the at least one user to be recommended based on the recommended weight and a preset rule, and recommending the recommended user to the target user.
In one or more possible embodiments, before the removing the hot user in the first focus list of the target user and obtaining the second focus list, the method further includes:
acquiring an implicit focus list of a target user based on a preset algorithm;
the first attention list is a union set of the explicit attention list and the implicit attention list, the explicit attention list is an attention list of the target user displayed on the social platform, and the implicit attention list is a user set aiming at the attention of the target user on the social platform in an indirect attention mode.
In one or more possible embodiments, the obtaining the implicit attention list and the explicit attention list of the target user based on the preset algorithm includes:
obtaining a published text of the target user within a preset time period;
extracting user reference words in the published text; wherein the user reference includes at least one of: user name, user nickname, topic;
and determining an implicit user corresponding to the user reference word based on the user reference word, and constructing the implicit attention list based on the implicit user.
In one or more possible embodiments, the obtaining the implicit attention list and the explicit attention list of the target user based on the preset algorithm includes:
judging the liveness of the target user;
determining that the target user is an inactive user under the condition that the activity level of the target user is smaller than an activity level threshold value;
constructing a social network relation network of the target user based on the mutual attention list of the target user;
acquiring active users in the social network relation network based on the social network relation network;
based on the active users, obtaining similar users; the similar users are users with the similarity between the active users and the target users exceeding a similarity threshold;
and acquiring an implicit attention list of the similar user based on the similar user, and taking the implicit attention list of the similar user as the implicit attention list of the target user.
In one or more possible embodiments, the calculating, based on the to-be-recommended user list, a recommendation weight of each to-be-recommended user in the to-be-recommended user list includes:
acquiring an intersection list of the attention list of the user to be recommended and a second attention list of the target user;
Within a preset time length, calculating the interaction weight of the user to be recommended and each user in the intersection list;
and accumulating the interaction weights to obtain the recommendation weights of the users to be recommended.
In one or more possible embodiments, the calculating the interaction weight between the user to be recommended and each user in the intersection list within the preset duration includes:
based on a preset time granularity, configuring attenuation weights for the initial interaction weights in the preset time length to obtain an interaction attenuation function;
acquiring interaction data of the user to be recommended and each user in the intersection list, wherein the interaction data comprises interaction type, interaction frequency and interaction timeliness;
and acquiring the interaction weight based on the interaction decay function and the interaction data.
In one or more possible embodiments, the calculating the interaction weight between the user to be recommended and each user in the intersection list within the preset duration includes:
calculating an interaction time factor TF (t) of the user to be recommended with each user in the intersection list, wherein:
wherein T represents time when kth interaction occurs after the preset time period is divided based on first time granularity, and is 1 Representing an interaction end time based on a second time granularity, the T 0 Representing an interaction start time based on the second time granularity;
acquiring an interaction type of interaction between the user to be recommended and each user in the intersection list and an interaction type weight V (i, j) corresponding to the interaction type, wherein the value of V (i, j) is an integer between 1 and 5;
based on the interaction time factor TF (t) and the interaction type weight V (i, j), acquiring a kth interaction weight W of the user to be recommended and each user in the intersection list within the preset duration k (i, j, t), wherein:
W k (i，j，t)＝V(i，j，k)×TF(t)；
based on the interaction weight W k (i, j, t) calculating the recommendation weight W (i, j), wherein:
wherein the K represents a total number of interactions of the recommended user with each user in the intersection list within the preset time period.
In a second aspect, embodiments of the present application provide an apparatus for recommending social users, the apparatus comprising:
the hotspot removing module is used for removing the hotspot users in the first attention list of the target user to obtain a second attention list; the hot spot users are users with the interaction number exceeding an interaction threshold value and/or the attention number exceeding an attention threshold value in the social platform;
The acquisition list module is used for acquiring a user list to be recommended from a user database of the social platform based on the second attention list; the user to be recommended list comprises at least one user to be recommended, and the attention list of the user to be recommended comprises at least one user in the second attention list;
the weight calculating module is used for calculating the recommendation weight of each user to be recommended in the user list to be recommended based on the user list to be recommended;
and the recommending user module is used for acquiring recommending users in the at least one user to be recommended based on the recommending weight and a preset rule and recommending the recommending users to the target user.
In a third aspect, embodiments of the present application provide a computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the above-described method steps.
In a fourth aspect, embodiments of the present application provide an electronic device, which may include: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the above-mentioned method steps.
The technical scheme provided by some embodiments of the present application has the beneficial effects that at least includes: the user list to be recommended is obtained by removing the second attention list of the hot users, the recommendation weight of each user to be recommended in the user list to be recommended is calculated, the recommendation user is determined based on the recommendation weight and a preset rule, and the recommendation user is recommended to the target user, so that personalized recommendation for the target user can be realized, the recommendation effect of network social user recommendation is remarkably improved, the probability of recommending interested recommendation users for the target user is improved, and the social friend network of the target user is further mined.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of recommending social users according to an embodiment of the present application;
FIG. 2 is an interface schematic diagram of an explicit attention list of a target user according to an embodiment of the present application;
fig. 3 is a schematic flow chart of establishing an implicit attention list of a target user according to an embodiment of the present application;
FIG. 4 is a flowchart of calculating recommendation weights according to an embodiment of the present application;
FIG. 5 is a schematic illustration of intersection of a target user with a focus list of users to be recommended according to an embodiment of the present application;
FIG. 6 is a flowchart illustrating an interactive weight calculation according to an embodiment of the present disclosure;
FIG. 7 is a schematic flow chart of another method for recommending social users according to an embodiment of the present application;
FIG. 8 is a schematic structural diagram of a social network relationship network of a target user according to an embodiment of the present application;
FIG. 9 is a flowchart of another method for calculating recommendation weights according to an embodiment of the present application;
FIG. 10 is a schematic diagram of an apparatus for recommending social friends according to an embodiment of the present application;
fig. 11 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
In the description of the present application, it should be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In the description of the present application, it is to be understood that the terms "comprise" and "have," and any variations thereof, are intended to cover non-exclusive inclusions, unless otherwise specifically defined and defined. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus. The specific meaning of the terms in this application will be understood by those of ordinary skill in the art in a specific context. Furthermore, in the description of the present application, unless otherwise indicated, "a plurality" means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.
The present application is described in detail with reference to specific examples.
In one embodiment, as shown in FIG. 1, a proposed social user recommendation method, which may be implemented in dependence on a computer program, may be run on an image source tracking device based on von Neumann systems. The computer program may be integrated in the application or may run as a stand-alone tool class application.
Specifically, the social user recommending method comprises the following steps:
s101, removing the hot users in the first attention list of the target user to obtain a second attention list.
The target user can be understood as any user account on the social platform, and the user account can be an identification with unique directivity such as a device number, a mobile phone number, a user name and the like, for example, an account of one id called three on a microblog. The social platform can be understood as a service platform with social attribute functions such as a social function, a friend function, a posting text function and the like in the prior art, such as microblog, tremble sound or result.
The hot-spot user may be understood as an account number in which the number of interactions in the social platform exceeds an interaction threshold and/or the number of interests exceeds an attention threshold, for example, the social platform is a microblog, the number of interactions includes the sum of praise, forwarding and comments, the interaction threshold is 10000 for a single microblog text, the number of interests is the sum of the number of people focusing on the user, the attention threshold is 100 ten thousand of the number of people focusing on the user, and the hot-spot user may be understood as a user account number satisfying one or more of the above conditions, for example, a user account number representing a star.
In one embodiment, removing the hot users in the first attention list of the target user, before obtaining the second attention list, further includes: acquiring an implicit focus list of a target user based on a preset algorithm; the first attention list is a union set of an explicit attention list and an implicit attention list, the explicit attention list is an attention list of a target user displayed on the social platform, and the implicit attention list is a user set aiming at the target user on the social platform and paying attention in an indirect attention mode.
As shown in fig. 2, an interface schematic diagram of an explicit attention list of a target user according to an embodiment of the present application includes a target user Zhang three 201, where the explicit attention list is all user identifiers included in an attention list 202. In other words, when the terminal device detects the triggering condition on the [ focus ] control, the focus list 202 is displayed on the display interface, and the focus list 202 is an explicit focus list.
It should be noted that, when the target user pays attention to another user, it can be understood that a node representing the target user and a node representing the other user establish a unidirectional link, the link direction is a node pointing from the node of the target user to the other user, and the above payingattention mode is defined as direct payattention; when the frequency of a certain user reference word in the published text exceeds the indirect attention threshold value, but the target user does not establish a unidirectional link or a bidirectional link with the user reference word, the attention mode is defined as indirect attention. The user reference includes at least one of: user name, user nickname, topic, e.g., user name: week xx, user nickname: jayChou, topic: # you are My UleMei#, which are user-referents for the entity person week xx and the account JayChou of week xx on the social platform. In other words, indirect focus may be understood as the number of times that the target user notes the user reference word representing the entity person week xx in the posting text that includes text data left in the social platform by all target users such as private letters of the social users on the social platform, talk content posted on their own accounts, comment content of comment areas under talk content of other social users, and the like, exceeds the indirect focus threshold, but does not directly focus on the account number of week xx on the social platform.
As shown in fig. 3, a flowchart of obtaining an implicit attention list of a target user according to an embodiment of the present application includes the following specific steps:
s301, obtaining a published text of a target user in a preset time period;
for example, the preset time period is a posting text of the target user from the 1 st of 2020 to the 31 st of 2020, the posting text includes text data left on the social platform by all target users such as private letters of social users on the social platform, posting speaking contents of own accounts, comment contents of comment areas under speaking contents of other social users, and the like, for example, speaking contents of the target user on microblogs are: # you are My you Lemei# today the same period of the myth is drunk as the strawberry flavor.
S302, extracting user reference words in the published text;
the user reference includes at least one of: user name, user nickname, topic. For example, the speaking content of the target user in the microblog is: # you are my you drink today with the same strawberry flavor, wherein the topic # you are my you and the user nickname "my week" are both the entity character week xx and the user reference of the account jaycou of the social platform week xx.
The extraction algorithm of the step can adopt a natural language processing method, firstly, part-of-speech segmentation and labeling are carried out on the content of the release text, and corresponding entity characters are matched in a preset natural language word stock based on the labeling.
S303, determining an implicit user corresponding to the user reference word based on the user reference word, and constructing an implicit attention list based on the implicit user.
The technical scheme provided by some embodiments of the present application has the beneficial effects that at least includes: the implicit user list is constructed by extracting the implicit user from the release text of the user, and the first attention list of the target user is formed by combining the implicit user list and the explicit user list, so that the degree of interest and hobbies of the target user are improved, the establishment of the attention list of the target user is perfected, and deviation and omission caused when the recommended user is matched for the target user in the follow-up process are avoided.
S102, acquiring a user list to be recommended from a user database of the social platform based on the second attention list.
It should be noted that the user to be recommended list includes at least one user to be recommended, and the attention list of the user to be recommended includes at least one user in the second attention list.
For example, the second attention list of the target user Zhang three includes a user A, a user B, a user C and a user D, the server of the social platform matches at least one user to be recommended in the user database through the second attention list, the attention list of the user to be recommended includes at least one or more of the user A, the user B, the user C and the user D, for example, matches to the user to be recommended 1 and the user to be recommended 2, the attention list of the user to be recommended 1 includes the user A, the attention list of the user to be recommended 2 includes the user B and the user D, and the user list to be recommended is constructed based on the at least one user to be recommended.
It can be understood that the attention list of the user to be recommended is acquired based on the above-mentioned acquisition procedure of the second attention list of the target user, in other words, the attention list of the user to be recommended is also composed of a union of the explicit attention list and the implicit attention list of the user to be recommended.
S103, calculating the recommendation weight of each user to be recommended in the user list to be recommended based on the user list to be recommended.
As shown in fig. 4, a flowchart of calculating a recommendation weight according to an embodiment of the present application includes:
s1031, acquiring an intersection list of a focus list of a user to be recommended and a second focus list of a target user;
as shown in fig. 6, in an intersection schematic diagram of a target user and a to-be-recommended user attention list provided in the embodiment of the present application, a second attention list of the target user, which is open to three, includes a user a, a user B, a user C, and a user D, a server of a social platform matches, in a user database, to-be-recommended user 1 and to-be-recommended user 2 through the second attention list, the to-be-recommended user 1 attention list includes a user a, the to-be-recommended user 2 attention list includes a user B and a user D, and the to-be-recommended user list includes a user 1 and a user 2; acquiring an intersection list of a focus list of the user 1 to be recommended and a second focus list of the target user, wherein the intersection list comprises a user A, and the intersection list of the focus list of the user 2 to be recommended and the second focus list of the target user comprises a user B and a user D.
S1032, calculating the interaction weight of the user to be recommended and each user in the intersection list within the preset time length;
for example, as shown in fig. 5, calculating the interaction weight of the user to be recommended and each user in the intersection list within the preset time period can be understood as calculating the interaction weight of the user to be recommended 1 and the user a and the interaction weight of the user to be recommended 2 and the user B and the user D respectively within the preset time period.
As shown in fig. 6, a flowchart of calculating an interaction weight according to an embodiment of the present application includes:
s10321, configuring attenuation weights for initial interaction weights in a preset duration based on a preset time granularity to obtain an interaction attenuation function;
the preset duration may be set based on a recommendation period, for example, the social platform recommends social users for target users every other week, the preset duration may be one week, a recommendation weight of at least one user to be recommended in each week is calculated, and based on the recommendation weight and a preset rule, a recommended user in the at least one user to be recommended is obtained and recommended to the target users.
Based on a preset time granularity, configuring attenuation weights for the initial interaction weights within a preset time length, wherein,
N(t)＝N 0 e -at ；
N (t) is an interactive decay function over t unit times after dividing based on a preset time granularity within a preset time period, N 0 For the initial interaction weight, a is the decay weight.
For example, the time granularity is day, the preset duration is one week, and N (6) is the interactive decay function of the sixth day.
S10322, acquiring interaction data of the user to be recommended and each user in the intersection list;
the interaction data includes an interaction type, an interaction frequency and an interaction time, for example, the interaction type includes that a user to be recommended leaves a message for the user in an intersection list, the user to be recommended leaves a message for the user in the intersection list and the user in the intersection list to be mutually praised, the interaction frequency is the frequency of sending interaction between the user to be recommended and the user in the intersection list in unit time after the preset time is divided based on time granularity, and the interaction time is the time from the beginning to the end of one interaction.
S10323, acquiring interaction weight based on the interaction decay function and the interaction data.
For example, the time granularity is day, and the preset time period is one week; the method comprises the steps that on the first day, a server obtains initial interaction weight 30 based on interaction data of a user to be recommended on the first day and users in an intersection list, and obtains interaction weight of the first day as 30 based on an interaction attenuation function; the next day, the server obtains initial interaction weight 25 based on the interaction data of the user to be recommended and the user in the intersection list in the next day, and obtains the interaction weight of 18 in the next day based on the interaction decay function; on the third day, the server obtains an initial interaction weight 32 based on the interaction data of the user to be recommended on the third day and the user in the intersection list, obtains an interaction weight of 19 on the third day based on the interaction decay function, and the like, and in a preset period of time, the interaction weights of the user to be recommended and the user in the intersection list are respectively 30, 18, 19, 15, 14, 17 and 25, and the interaction weight of the final user to be recommended and the user in the intersection list is 138.
In summary, based on steps S10321, S10322, and S10323, the interaction weight of the user to be recommended and each user in the intersection list can be obtained. The technical scheme provided by some embodiments of the present application has the beneficial effects that at least includes: based on the interaction mode diversity included in the interaction data, initial interaction weights are obtained, different affinity relations between users displayed by different interaction modes on a social platform are effectively utilized, and accuracy of recommending social users to target users is improved; and the attenuation weights are configured based on time, so that the calculation of the interaction weights and even the recommendation weights is more reasonable and reliable.
It can be appreciated that the method of how to obtain the interaction weight is not limited in this application.
S1033, accumulating the interaction weights to obtain recommendation weights of the users to be recommended.
For example, as shown in fig. 5, the interaction weight of the user 1 to be recommended and the user a in the intersection list is 138, and the recommendation weight of the user 1 to be recommended is 138; the interaction weight of the user 2 to be recommended and the user B in the intersection list is 50, the interaction weight of the user 2 to be recommended and the user D is 120, and the recommendation weight of the user 2 to be recommended is 170.
In summary, the recommendation weight of the user to be recommended is calculated based on steps S1031, S1032, and S1033.
S104, based on the recommendation weight and a preset rule, acquiring a recommended user of at least one user to be recommended, and recommending the recommended user to a target user.
The preset rule may be that the larger the value is according to the value of the recommendation weight, the higher the recommendation priority of the user to be recommended is, and the recommended user of the user to be recommended is determined based on the recommendation name of the social platform. For example, if the recommendation weight of the user 1 to be recommended is 138, the recommendation weight of the user 2 to be recommended is 170, and the recommendation name of the social platform is 1, the user 2 to be recommended is recommended to the target user as the recommended user.
In another embodiment, the preset rule is to judge that the user to be recommended is a recommended user when the recommendation weight of the user to be recommended is greater than the recommendation threshold. For example, if the recommendation threshold is 90, the recommendation weight of the user 1 to be recommended is 138, and the recommendation weight of the user 2 to be recommended is 170, the user 1 to be recommended and the user 2 to be recommended are recommended to the target user as recommended users.
The technical scheme provided by some embodiments of the present application has the beneficial effects that at least includes: the user list to be recommended is obtained by removing the second attention list of the hot users, the recommendation weight of each user to be recommended in the user list to be recommended is calculated, the recommendation user is determined based on the recommendation weight and a preset rule, and the recommendation user is recommended to the target user, so that personalized recommendation for the target user can be realized, the recommendation effect of network social user recommendation is remarkably improved, the probability of recommending interested recommendation users for the target user is improved, and the social friend network of the target user is further mined.
As shown in fig. 7, a flowchart of another method for recommending social users according to an embodiment of the present application includes the following specific steps:
s701, judging the activity degree of the target user.
In one embodiment, the method for judging the activity level of the target user may be: the frequency of interactions of the target user with other social users on the social platform, such as 30 times per week, is determined, including privately believing, leaving messages, or praying to the other social users.
In another embodiment, the method for judging the activity of the target user may be to calculate a ratio of the attention number of the target user to the attention number of the target user, where the attention number of the target user generally reflects the participation degree of the target user in the social platform, the attention number of the target user generally reflects the authority degree and the favorite degree of the target user, and when the attention number/the attention number is greater than 1, the target user is primarily judged to be an active user, and when the attention number/the attention number is less than 1, the target user is primarily judged to be a hotspot character, and whether the target user is an active user needs to be further judged. The above further judgment may employ the judgment method in the first embodiment.
S702, determining that the target user is an inactive user under the condition that the activity degree of the target user is smaller than an activity degree threshold value.
Whether the target user is an inactive user is determined based on the method in the above embodiment. For example, the number of interactions with other social users per week is defined as 35 times as the liveness threshold, and the interaction frequency of the target user is 30 per week, and the target user is judged as an inactive user.
In another embodiment, when the target user is judged to be an active user, social users are recommended to the target user according to the method shown in fig. 1 to 6.
S703, constructing a social network relation network of the target user based on the mutual attention list of the target user;
the mutual attention list can be understood as a user set based on all users on the social platform, which establish bidirectional attention with the target user. In order to conveniently describe the social relationship of the target user, the social network relationship of the target user is conveniently mined and calculated, and a social network relationship network of the target user is constructed.
Fig. 8 is a schematic structural diagram of a social network relationship network of a target user according to an embodiment of the present application. When the target user pays attention to another user, it can be understood that a node representing the target user establishes a unidirectional link with a node representing the other user, and the link direction is a node pointed to the other user by the node of the target user. When the target user and another user are interested in each other, it can be understood that the node representing the target user establishes a bi-directional link with the node representing the other user.
S704, acquiring active users in the social network relation network based on the social network relation network.
The method for determining active users is based on the step S704, in other words, active users of the social network relationship network of the target user are obtained.
S705, based on the active users, obtaining similar users.
Similar users may be understood as users whose similarity to the active user and the target user exceeds a similarity threshold. The judgment algorithm of the similar user can be obtained by a friend algorithm (Friend of a Friend algorithm, FOFA) of friends, which judges the similarity mainly based on the number of common friends between the target user and the active user. The judgment algorithm of the similar users can also be an Adamic/Adar Algorithm (AA) which mainly represents the similarity between nodes, i.e. the similarity between the target user and the active user, based on a weighted sum of common attributes between the computing nodes.
S706, acquiring an implicit attention list of a similar user based on the similar user, wherein the implicit attention list of the similar user is used as an implicit attention list of a target user.
The implicit focus list is a set of users focused on the social platform in an indirect focus manner by similar users. When the frequency of a user reference word in the published text exceeds the indirect attention threshold value, but the similar user does not establish a unidirectional link or a bidirectional link with the user reference word, the attention mode is defined as indirect attention.
The method for obtaining the invisible attention list of the similar user refers to steps S301, S302 and S303 shown in fig. 3, and will not be described herein.
The technical scheme provided by some embodiments of the present application has the beneficial effects that at least includes: when the target user is judged to be an inactive user, the implicit attention list of the similar user is utilized to replace the implicit attention list of the target user, so that the situation that the implicit user cannot be extracted and the implicit attention list of the target user cannot be constructed because the release text of the target user is too small is avoided, and the success rate and effect of recommending the social user to the target user are improved.
S707, acquiring an explicit attention list of the target user, and taking the union of the explicit attention list and the implicit attention list as a first attention list.
As shown in fig. 2, an interface schematic diagram of an explicit attention list of a target user according to an embodiment of the present application includes a target user Zhang three 201, where the explicit attention list is all user identifiers included in an attention list 202. In other words, when the terminal device detects the triggering condition on the [ focus ] control, the focus list 202 is displayed on the display interface, and the focus list 202 is an explicit focus list.
The method comprises the steps that a server of the social platform obtains an explicit attention list of a target user, obtains an implicit attention list through a similar user, and takes the explicit attention list and the implicit attention list as a first attention list.
S708, removing the hot users in the first attention list of the target user to obtain a second attention list.
A hotspot user is a user whose number of interactions in the social platform exceeds an interaction threshold and/or whose number of interests exceeds a degree of interest threshold. The specific content of step 708 refers to step S101 shown in fig. 1, and will not be described here.
S709, acquiring a user list to be recommended from a user database of the social platform based on the second attention list.
The user to be recommended list comprises at least one user to be recommended, and the attention list of the user to be recommended comprises at least one user in a second attention list. The specific content of step 709 refers to step S102 shown in fig. 1, and will not be described here.
S710, calculating the recommendation weight of each user to be recommended in the list of the users to be recommended based on the list of the users to be recommended.
As shown in fig. 9, another flowchart of acquiring recommendation weights according to an embodiment of the present application includes the following specific steps:
s7101, calculating interaction time factor TF (t) of the user to be recommended and each user in the intersection list, wherein:
Wherein T represents time of occurrence of kth interaction after dividing preset time based on first time granularity, T 1 Representing an interaction end time based on a second time granularity, T 0 Representing interactions based on a second temporal granularityStart time.
For example, the preset duration is one week, the first time granularity is day, the time T of the kth interaction is 3 days, the second time granularity is 24 hours, and the interaction ending time T 1 For 8 am, interaction start time T 0 Is 7 am.
S7102, an interaction type of interaction between the user to be recommended and each user in the intersection list is obtained, and an interaction type weight V (i, j) corresponding to the interaction type is obtained.
V (i, j) is an integer with a value of 1-5, and the larger the value of V (i, j) is, the higher the social affinity degree between the target user and each user in the intersection list is, for example, the weight corresponding to the interaction type that the target user and the users in the intersection list comment on each other is 5, and the weight corresponding to the interaction type that the target user praise the users in the intersection list is 5.
S7103, based on the interaction time factor TF (t) and the interaction type weight V (i, j), acquiring the kth interaction weight W of the user to be recommended and each user in the intersection list within a preset duration k (i, j, t), wherein:
W k (i，j，t)＝V(i，j，k)×TF(t)
s7104 based on interaction weight W k (i, j, t) calculating a recommendation weight W (i, j), wherein:
wherein K represents the total number of interactions between the recommended user and each user in the intersection list within a preset time period, for example, the total number of interactions K is 40 times within a preset time period of one week.
The technical scheme provided by some embodiments of the present application has the beneficial effects that at least includes: the algorithm provided by the embodiment is simpler, the computing resources of the server are saved, the weight corresponding to the interaction type is improved, namely, the intimacy of the user to be recommended and the user in the intersection list is more concerned, the indirect intimacy degree between the target user and the recommended user is improved, and the method has good predictability for establishing the direct intimacy between the target user and the recommended user.
S711, based on the recommendation weight and a preset rule, acquiring a recommended user of at least one user to be recommended, and recommending the recommended user to the target user.
The preset rule may be that the larger the value is according to the value of the recommendation weight, the higher the recommendation priority of the user to be recommended is, and the recommended user of the user to be recommended is determined based on the recommendation name of the social platform. For example, if the recommendation weight of the user 1 to be recommended is 138, the recommendation weight of the user 2 to be recommended is 170, and the recommendation name of the social platform is 1, the user 2 to be recommended is recommended to the target user as the recommended user.
The specific content of step S711 refers to step S104 shown in fig. 1, and will not be described here.
The technical scheme provided by some embodiments of the present application has the beneficial effects that at least includes: when the target user is judged to be an inactive user, the implicit attention list of the target user is replaced by the implicit attention list of the similar user, so that the situation that the implicit user cannot be extracted and the implicit attention list of the target user cannot be constructed because the release text of the target user is too small is avoided; the user list to be recommended is obtained by removing the second attention list of the hot users, the recommendation weight of each user to be recommended in the user list to be recommended is calculated, the recommendation user is determined based on the recommendation weight and a preset rule, and the recommendation user is recommended to the target user, so that personalized recommendation for the target user can be realized, the recommendation effect of network social user recommendation is remarkably improved, the probability of recommending interested recommendation users for the target user is improved, and the social friend network of the target user is further mined.
The following are device embodiments of the present application, which may be used to perform method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.
Referring to fig. 10, a schematic structural diagram of a recommended social user device according to an exemplary embodiment of the present application is shown. The recommended social user device may be implemented as all or part of the device by software, hardware, or a combination of both. The recommended social user device includes a remove hot spot module 1001, an acquire list module 1002, a calculate weights module 1003, and a recommend user module 1004.
A hotspot removal module 1001, configured to remove a hotspot user in the first attention list of the target user, to obtain a second attention list; the hot spot users are users with the interaction number exceeding an interaction threshold value and/or the attention number exceeding an attention threshold value in the social platform;
the acquisition list module 1002 acquires a list of users to be recommended from a user database of the social platform based on the second attention list; the user to be recommended list comprises at least one user to be recommended, and the attention list of the user to be recommended comprises at least one user in the second attention list;
a calculation weight module 1003, configured to calculate a recommendation weight of each user to be recommended in the list of users to be recommended based on the list of users to be recommended;
And a recommending user module 1004, configured to obtain a recommending user of the at least one user to be recommended based on the recommending weight and a preset rule, and recommend the recommending user to the target user.
Optionally, the recommending social user device further includes:
the acquisition module is used for acquiring an implicit attention list and an explicit attention list of a target user based on a preset algorithm;
the first attention list is a union set of the explicit attention list and the implicit attention list, the explicit attention list is an attention list of the target user displayed on the social platform, and the implicit attention list is a user set aiming at the attention of the target user on the social platform in an indirect attention mode.
Optionally, the acquiring module includes:
the preset unit is used for acquiring the publication text of the target user in a preset time period;
an extraction unit for extracting user reference words in the published text; wherein the user reference includes at least one of: user name, user nickname, topic;
and the determining unit is used for determining an implicit user corresponding to the user reference word based on the user reference word and constructing the implicit attention list based on the implicit user.
Optionally, the acquiring module includes:
a judging unit that judges the activity level of the target user;
the inactivity degree unit is used for determining that the target user is an inactive user under the condition that the activity degree of the target user is smaller than an activity degree threshold value;
the construction unit is used for constructing a social network relation network of the target user based on the mutual attention list of the target user;
acquiring an active user unit, and acquiring active users in the social network relation network based on the social network relation network;
acquiring a similar user unit, and acquiring the similar user based on the active user; the similar users are users with the similarity between the active users and the target users exceeding a similarity threshold;
and the replacing unit is used for acquiring the implicit attention list of the similar user based on the similar user, and taking the implicit attention list of the similar user as the implicit attention list of the target user.
Optionally, the calculating weight module 1003 includes:
an intersection unit for acquiring an intersection list of the attention list of the user to be recommended and a second attention list of the target user;
the interaction weight unit is used for calculating the interaction weight of the user to be recommended and each user in the intersection list within a preset time length;
And the accumulation unit is used for accumulating the interaction weights to obtain the recommendation weights of the users to be recommended.
Optionally, the interaction weight unit is specifically configured to:
based on a preset time granularity, configuring attenuation weights for the initial interaction weights in the preset time length to obtain an interaction attenuation function;
acquiring interaction data of the user to be recommended and each user in the intersection list, wherein the interaction data comprises interaction type, interaction frequency and interaction timeliness;
and acquiring the interaction weight based on the interaction decay function and the interaction data.
Optionally, the calculating weight module 1003 includes:
a calculating aging unit for calculating an interaction aging factor TF (t) of the user to be recommended and each user in the intersection list, wherein:
wherein T represents time when kth interaction occurs after the preset time period is divided based on first time granularity, and is 1 Representing an interaction end time based on a second time granularity, the T 0 Representing an interaction start time based on the second time granularity;
the interaction type obtaining unit is used for obtaining interaction types of interaction between the user to be recommended and each user in the intersection list and an interaction type weight V (i, j) corresponding to the interaction types, wherein the value of V (i, j) is an integer between 1 and 5;
The interaction weight acquisition unit is used for acquiring the kth interaction weight W of the user to be recommended and each user in the intersection list within the preset duration based on the interaction time factor TF (t) and the interaction type weight V (i, j) k (i, j, t), wherein:
W k (i，j，t)＝V(i，j，k)×TF(t)；
a recommendation weight unit is calculated and based on the interaction weight W k (i, j, t) calculating the recommendation weight W (i, j), wherein:
wherein the K represents a total number of interactions of the recommended user with each user in the intersection list within the preset time period.
It should be noted that, when executing the social recommendation user method, the social recommendation user device provided in the foregoing embodiment only uses the division of the foregoing functional modules to illustrate, in practical application, the foregoing functional allocation may be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the recommended social user device provided in the above embodiment belongs to the same concept as the recommended social user method embodiment, and the implementation process is embodied in the method embodiment, which is not described herein again.
According to the method and the system, the user list to be recommended is obtained by removing the second attention list of the hot user, the recommendation weight of each user to be recommended in the user list to be recommended is calculated, the recommended user is determined based on the recommendation weight and the preset rule, and the recommended user is recommended to the target user, so that personalized recommendation for the target user can be realized, the recommendation effect of network social user recommendation is remarkably improved, the probability of recommending interested recommended users for the target user is improved, and the social friend network of the target user is further mined.
The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments.
The embodiment of the present application further provides a computer storage medium, where a plurality of instructions may be stored, where the instructions are suitable for being loaded by a processor and executed by the processor, where the specific execution process may refer to the specific description of the embodiment shown in fig. 1 to 9, and the details are not repeated herein.
The present application further provides a computer program product, where at least one instruction is stored, where the at least one instruction is loaded by the processor and executed by the processor to perform the image source tracking method according to the embodiment shown in fig. 1 to fig. 9, and the specific execution process may refer to the specific description of the embodiment shown in fig. 1 to fig. 9, which is not repeated herein.
Referring to fig. 11, a schematic structural diagram of an electronic device is provided in an embodiment of the present application. As shown in fig. 11, the electronic device 1100 may include: at least one processor 1101, at least one network interface 1104, a user interface 1103, a memory 1105, at least one communication bus 1102.
Wherein communication bus 1102 is used to facilitate connection communications among the components.
The user interface 1103 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 1103 may further include a standard wired interface and a wireless interface.
Wherein the processor 1101 may comprise one or more processing cores. The processor 1101 connects various portions of the overall server 1100 using various interfaces and lines, and performs various functions of the server 1100 and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 1105, and invoking data stored in the memory 1105. Alternatively, the processor 1101 may be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 1101 may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU), and a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 1101 and may be implemented by a single chip.
The Memory 1105 may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 1105 includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). Memory 1105 may be used to store instructions, programs, code, sets of codes, or sets of instructions. The memory 1105 may include a stored program area that may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, etc., and a stored data area; the storage data area may store data or the like referred to in the above respective method embodiments. The memory 1105 may also optionally be at least one storage device located remotely from the processor 1101. As shown in FIG. 11, an operating system, a network communication module, a user interface module, and a social user recommendation application may be included in memory 1105 as one type of computer storage medium.
In the electronic device 1100 shown in fig. 11, the user interface 1103 is mainly used for providing an input interface for a user, and acquiring data input by the user; and the processor 1101 may be configured to invoke the recommended social friend application stored in the memory 1105, and specifically perform the following operations:
Removing hot users in the first attention list of the target user to obtain a second attention list; the hot spot users are users with the interaction number exceeding an interaction threshold value and/or the attention number exceeding an attention threshold value in the social platform;
acquiring a user list to be recommended from a user database of the social platform based on the second attention list; the user to be recommended list comprises at least one user to be recommended, and the attention list of the user to be recommended comprises at least one user in the second attention list;
calculating the recommendation weight of each user to be recommended based on the list of the users to be recommended;
and acquiring a recommended user in the at least one user to be recommended based on the recommended weight and a preset rule, and recommending the recommended user to the target user.
In one possible embodiment, the processor 1101 further performs the following operations before the removing the hot spot user in the first focus list of the target user, and obtaining the second focus list:
acquiring an implicit focus list of a target user based on a preset algorithm;
the first attention list is a union set of the explicit attention list and the implicit attention list, the explicit attention list is an attention list of the target user displayed on the social platform, and the implicit attention list is a user set aiming at the attention of the target user on the social platform in an indirect attention mode.
In one possible embodiment, the processor 1101 performs the obtaining the implicit attention list and the explicit attention list of the target user based on the preset algorithm, specifically performs the following operations:
obtaining a published text of the target user within a preset time period;
extracting user reference words in the published text; wherein the user reference includes at least one of: user name, user nickname, topic;
and determining an implicit user corresponding to the user reference word based on the user reference word, and constructing the implicit attention list based on the implicit user.
In one possible embodiment, the processor 1101 performs the obtaining the implicit attention list and the explicit attention list of the target user based on the preset algorithm, specifically performs the following operations:
judging the liveness of the target user;
determining that the target user is an inactive user under the condition that the activity level of the target user is smaller than an activity level threshold value;
constructing a social network relation network of the target user based on the mutual attention list of the target user;
acquiring active users in the social network relation network based on the social network relation network;
Based on the active users, obtaining similar users; the similar users are users with the similarity between the active users and the target users exceeding a similarity threshold;
and acquiring an implicit attention list of the similar user based on the similar user, and taking the implicit attention list of the similar user as the implicit attention list of the target user.
In one possible embodiment, the processor 1101 performs the calculating, based on the list of users to be recommended, a recommendation weight of each of the users to be recommended, specifically performing the following operations:
acquiring an intersection list of the attention list of the user to be recommended and a second attention list of the target user;
within a preset time length, calculating the interaction weight of the user to be recommended and each user in the intersection list;
and accumulating the interaction weights to obtain the recommendation weights of the users to be recommended.
In one possible embodiment, the processor 1101 performs the following operations in detail, where the interaction weight between the user to be recommended and each user in the intersection list is within the calculated preset time period:
based on a preset time granularity, configuring attenuation weights for the initial interaction weights in the preset time length to obtain an interaction attenuation function;
Acquiring interaction data of the user to be recommended and each user in the intersection list, wherein the interaction data comprises interaction type, interaction frequency and interaction timeliness;
and acquiring the interaction weight based on the interaction decay function and the interaction data.
In one possible embodiment, the processor 1101 performs the following operations in detail, where the interaction weight between the user to be recommended and each user in the intersection list is within the calculated preset time period:
calculating an interaction time factor TF (t) of the user to be recommended with each user in the intersection list, wherein:
wherein T represents time when kth interaction occurs after the preset time period is divided based on first time granularity, and is 1 Representing an interaction end time based on a second time granularity, the T 0 Representing an interaction start time based on the second time granularity;
acquiring an interaction type of interaction between the user to be recommended and each user in the intersection list and an interaction type weight V (i, j) corresponding to the interaction type, wherein the value of V (i, j) is an integer between 1 and 5;
based on the interaction time factor TF (t) and the interaction type weight V (i, j), acquiring a kth interaction weight W of the user to be recommended and each user in the intersection list within the preset duration k (i, j, t), wherein:
W k (i，j，t)＝V(i，j，k)×TF(t)；
based on the interaction weight W k (i, j, t) calculating the recommendation weight W (i, j), wherein:
wherein the K represents a total number of interactions of the recommended user with each user in the intersection list within the preset time period.
According to the method and the system, the user list to be recommended is obtained by removing the second attention list of the hot user, the recommendation weight of each user to be recommended in the user list to be recommended is calculated, the recommended user is determined based on the recommendation weight and the preset rule, and the recommended user is recommended to the target user, so that personalized recommendation for the target user can be realized, the recommendation effect of network social user recommendation is remarkably improved, the probability of recommending interested recommended users for the target user is improved, and the social friend network of the target user is further mined.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a read-only memory, a random access memory, or the like.
The foregoing disclosure is only illustrative of the preferred embodiments of the present application and is not intended to limit the scope of the claims herein, as the equivalent of the claims herein shall be construed to fall within the scope of the claims herein.
Claims (8)
1. A method of recommending social users, the method comprising:
removing the hot users in the first attention list of the target user to obtain a second attention list; the hot spot users are users with the interaction number exceeding an interaction threshold value and/or the attention number exceeding an attention threshold value in the social platform;
acquiring a user list to be recommended from a user database of the social platform based on the second attention list; the user to be recommended list comprises at least one user to be recommended, and the attention list of the user to be recommended comprises at least one user in the second attention list;
calculating the recommendation weight of each user to be recommended based on the list of the users to be recommended;
based on the recommendation weight and a preset rule, acquiring a recommended user in the at least one user to be recommended, and recommending the recommended user to the target user;
before the removing the hot spot user in the first attention list of the target user and obtaining the second attention list, the method further comprises the following steps:
Acquiring an implicit focus list of a target user based on a preset algorithm;
the first attention list is a union set of the explicit attention list and the implicit attention list, the explicit attention list is an attention list of the target user displayed on the social platform, and the implicit attention list is a user set aiming at the target user paying attention in an indirect attention manner on the social platform;
the obtaining the implicit attention list and the explicit attention list of the target user based on the preset algorithm comprises the following steps:
obtaining a published text of the target user within a preset time period;
extracting user reference words in the published text; wherein the user reference includes at least one of: user name, user nickname, topic;
and determining an implicit user corresponding to the user reference word based on the user reference word, and constructing the implicit attention list based on the implicit user.
2. The method according to claim 1, wherein the obtaining the implicit focus list and the explicit focus list of the target user based on the preset algorithm includes:
judging the liveness of the target user;
Determining that the target user is an inactive user under the condition that the activity level of the target user is smaller than an activity level threshold value;
constructing a social network relation network of the target user based on the mutual attention list of the target user;
acquiring active users in the social network relation network based on the social network relation network;
based on the active users, obtaining similar users; the similar users are users with the similarity between the active users and the target users exceeding a similarity threshold;
and acquiring an implicit attention list of the similar user based on the similar user, and taking the implicit attention list of the similar user as the implicit attention list of the target user.
3. The method of claim 1, wherein the calculating the recommendation weight for each of the users to be recommended based on the list of users to be recommended comprises:
acquiring an intersection list of the attention list of the user to be recommended and a second attention list of the target user;
within a preset time length, calculating the interaction weight of the user to be recommended and each user in the intersection list;
and accumulating the interaction weights to obtain the recommendation weights of the users to be recommended.
4. The method of claim 3, wherein the calculating the interaction weight of the user to be recommended with each user in the intersection list for the preset time period includes:
based on a preset time granularity, configuring attenuation weights for the initial interaction weights in the preset time length to obtain an interaction attenuation function;
acquiring interaction data of the user to be recommended and each user in the intersection list, wherein the interaction data comprises interaction type, interaction frequency and interaction timeliness;
and acquiring the interaction weight based on the interaction decay function and the interaction data.
5. The method of claim 3, wherein the calculating the interaction weight of the user to be recommended with each user in the intersection list for the preset time period includes:
calculating an interaction time factor TF (t) of the user to be recommended with each user in the intersection list, wherein:
wherein t represents a baseAfter the preset time length is divided by the first time granularity, the time of occurrence of the kth interaction is the T 1 Representing an interaction end time based on a second time granularity, the T 0 Representing an interaction start time based on the second time granularity;
Acquiring an interaction type of interaction between the user to be recommended and each user in the intersection list and an interaction type weight V (i, j) corresponding to the interaction type, wherein the value of V (i, j) is an integer between 1 and 5;
based on the interaction time factor TF (t) and the interaction type weight V (i, j), acquiring a kth interaction weight W of the user to be recommended and each user in the intersection list within the preset duration k (i, j, t), wherein:
based on the interaction weight W k (i, j, t) calculating the recommendation weight W (i, j), wherein:
wherein the K represents a total number of interactions of the recommended user with each user in the intersection list within the preset time period.
6. An apparatus for recommending social users, the apparatus comprising:
the hotspot removing module is used for removing the hotspot users in the first attention list of the target user to obtain a second attention list; the hot spot users are users with the interaction number exceeding an interaction threshold value and/or the attention number exceeding an attention threshold value in the social platform;
the acquisition list module is used for acquiring a user list to be recommended from a user database of the social platform based on the second attention list; the user to be recommended list comprises at least one user to be recommended, and the attention list of the user to be recommended comprises at least one user in the second attention list;
The weight calculating module is used for calculating the recommendation weight of each user to be recommended in the user list to be recommended based on the user list to be recommended;
the recommending user module is used for acquiring recommending users in the at least one user to be recommended based on the recommending weight and a preset rule and recommending the recommending users to the target user;
the social user recommending device further comprises:
the acquisition module is used for acquiring an implicit attention list and an explicit attention list of a target user based on a preset algorithm;
the first attention list is a union set of the explicit attention list and the implicit attention list, the explicit attention list is an attention list of the target user displayed on the social platform, and the implicit attention list is a user set aiming at the target user paying attention in an indirect attention manner on the social platform;
the acquisition module comprises:
the preset unit is used for acquiring the publication text of the target user in a preset time period;
an extraction unit for extracting user reference words in the published text; wherein the user reference includes at least one of: user name, user nickname, topic;
And the determining unit is used for determining an implicit user corresponding to the user reference word based on the user reference word and constructing the implicit attention list based on the implicit user.
7. A computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the method steps of any one of claims 1 to 5.
8. An electronic device, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the method steps of any of claims 1-5.
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| CN111782963A (en) * | 2020-06-15 | 2020-10-16 | 中国铁塔股份有限公司 | Social network data mining method and system based on SNS and service equipment |
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