CN112087371B - Instant messaging group searching method, device, equipment and storage medium - Google Patents
Instant messaging group searching method, device, equipment and storage medium Download PDFInfo
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- CN112087371B CN112087371B CN202010947230.4A CN202010947230A CN112087371B CN 112087371 B CN112087371 B CN 112087371B CN 202010947230 A CN202010947230 A CN 202010947230A CN 112087371 B CN112087371 B CN 112087371B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L51/00—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail
- H04L51/04—Real-time or near real-time messaging, e.g. instant messaging [IM]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L12/00—Data switching networks
- H04L12/02—Details
- H04L12/16—Arrangements for providing special services to substations
- H04L12/18—Arrangements for providing special services to substations for broadcast or conference, e.g. multicast
- H04L12/185—Arrangements for providing special services to substations for broadcast or conference, e.g. multicast with management of multicast group membership
Abstract
The application discloses a method, a device, equipment and a storage medium for searching an instant messaging group, which relate to the technical field of computers, in particular to big data and intelligent searching technology. The specific implementation scheme is as follows: searching the group set according to the search condition in the search request to determine a plurality of target groups serving as search results; determining at least one ordering impact data for the target group; wherein the ordering impact data comprises at least one of: the user relationship affinity between the initiating user of the search request and the member users in the target group, the user interaction affinity between the initiating user and the member users, and the group liveness of the target group; and responding to the search request after sequencing each target group according to the sequencing influence data. According to the technical scheme, the group searching function in the instant messaging software is optimized, the group searching requirement of the user is met, and the user experience is improved.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method, an apparatus, a device, and a storage medium for searching for an instant messaging group.
Background
Instant Messaging (IM) application software is a network communication tool facing terminal users, and users can communicate between two or more people in real time by installing an Instant communication client or a webpage end. Under the scene that needs to communicate with many people, the group function of the instant messaging software can be used.
With the use of instant messaging software, a user may create numerous groups to communicate with, and when a specific group needs to be used for communication, the user may use the group search function of the instant messaging software to find the specific group.
When the number of groups in the instant messaging software is large, the number of search results recalled in group search may be large, and it is inconvenient for the user to accurately locate the group. Particularly, in the working instant messaging software or the instant messaging software used frequently for a long time, when the number of groups is large and the similarity among the members of the groups, the group names, the communication contents and the like is high, the number of the recalled search results is large and difficult to distinguish.
Disclosure of Invention
The application provides a searching method, a searching device, searching equipment and a storage medium of an instant messaging group so as to optimize the group searching function in instant messaging software.
In a first aspect, an embodiment of the present application provides a method for searching an instant messaging group, including:
searching the group set according to the search condition in the search request to determine a plurality of target groups serving as search results;
determining at least one ordering impact data for the target group; wherein the ordering impact data comprises at least one of: the user relationship affinity of an initiating user of the search request and member users in a target group, the user interaction affinity between the initiating user and the member users, and the group activity of the target group;
and responding to the search request after sequencing each target group according to the sequencing influence data.
In a second aspect, an embodiment of the present application further provides an apparatus for searching an instant messaging group, including:
the target group determining module is used for searching the group set according to the searching condition in the searching request so as to determine a plurality of target groups serving as searching results;
the sequencing influence data determining module is used for determining at least one sequencing influence data of the target group; wherein the ordering impact data comprises at least one of: the user relationship affinity of an initiating user of the search request and member users in a target group, the user interaction affinity between the initiating user and the member users, and the group activity of the target group;
and the search request response module is used for responding the search request after sequencing each target group according to the sequencing influence data.
In a third aspect, an embodiment of the present application further provides an electronic device, including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method of searching for an instant messaging group as described in any one of the embodiments of the present application.
In a fourth aspect, embodiments of the present application further provide a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions are configured to cause the computer to execute the method for searching an instant messaging group according to any one of the embodiments of the present application.
The embodiment of the application provides a searching method, a searching device, equipment and a storage medium of an instant messaging group, wherein a group set is searched according to a searching condition in a searching request so as to determine a plurality of target groups serving as a searching result; determining at least one ordering impact data for the target group; wherein the ordering impact data comprises at least one of: the user relationship affinity of an initiating user of the search request and member users in a target group, the user interaction affinity between the initiating user and the member users, and the group activity of the target group; and after sequencing the target groups according to the sequencing influence data, responding to the search request, optimizing the group search function in the instant messaging software, meeting the retrieval requirement of the user on the groups and improving the user experience.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present application, nor do they limit the scope of the present application. Other features of the present application will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
fig. 1 is a flowchart of a searching method for an instant messaging group according to an embodiment of the present disclosure;
fig. 2 is a flowchart of a method for searching an instant messaging group according to an embodiment of the present application;
fig. 3 is a flowchart of a searching method for an instant messaging group according to an embodiment of the present application;
fig. 4 is a flowchart of a method for searching an instant messaging group according to an embodiment of the present application;
fig. 5 is a structural diagram of a searching apparatus for an instant messaging group according to an embodiment of the present application;
fig. 6 is a block diagram of an electronic device for implementing a search method for an instant messaging group according to an embodiment of the present application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application to assist in understanding, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a flowchart illustrating a method for searching an instant messaging group according to an embodiment; the embodiment can be suitable for providing the group search function in various Instant Messaging (IM) software, and is particularly suitable for enterprise-level IM group search scenes. Compared with personal IM software in other living and entertainment fields, enterprise-level IM has the particularity of group names, group member distribution and search conditions of users due to the difference of enterprise organizational structures and communication requirements. Due to work needs, the number of work groups created by users is large, and there is usually a large overlap of group members, with only a small number of member differences. Based on the group condition, when the group search is carried out, the method specifically comprises the following steps:
1. for groups with completely different liveness, searching and hitting are only carried out through the group member domain, and the retrieval intention of a user is difficult to meet in secondary sequencing;
2. due to the work requirement, secondary sequencing is performed based on the characteristics of group activity, group member scale and the like;
3. in a group search scenario, when a user inputs a search formula, such as a group name and a group member name, the result of searching a target group is generally specific, but a large amount of search results are often generated due to a large number of work groups.
In view of the above phenomena, the present application provides a method for searching an instant messaging group, which can provide a group search function for instant messaging software having the above group characteristics.
The method of the embodiment can be executed by a searching device of an instant messaging group, and the device can be realized in a software and/or hardware mode and can be integrated in electronic equipment such as a server or a user terminal.
As shown in fig. 1, the searching method for an instant messaging group provided in this embodiment includes:
and S110, searching the group set according to the search condition in the search request to determine a plurality of target groups serving as search results.
The searching request refers to a request for searching a group in instant messaging software by a user, and the user can input text content as a searching condition to perform group searching; the search condition may be at least one of speech content, subject, group name, group member name, etc. within the group as a search keyword; the target group refers to a group that matches a user search request.
Specifically, the group set is searched according to a search condition in a search request input by a user to determine a plurality of target groups of the search result. When a user inputs a search request, according to search conditions in the search request, a group set can be searched by search engine tools such as Solr or elastic search and the like based on search keyword (term) hit search or pinyin search, and a plurality of search results, i.e. a plurality of target groups, are recalled according to hit conditions and TF-IDF (term frequency-inverse document frequency) scores.
S120, determining at least one sequencing influence data of the target group; wherein the ordering impact data comprises at least one of: the user relationship affinity of the initiating user and the member users in the target group, the user interaction affinity between the initiating user and the member users and the group activity of the target group.
The user relationship affinity reflects the affinity relationship between the user and the user, and particularly the long-term affinity relationship with each user in the user group, may be the affinity of the initiating user for directly interacting with other member users in the target group, or may be an indirect relationship between the initiating user and other users. For example, the initiating user a and the other member users B, C, E, and F have a certain intimacy, and the member user B also have an intimacy, but the initiating user a and the member user C do not have an intimacy, and the user members E and F and the initiating user a and the member users B and F do not have an intimacy; because the member user B and the initiating user A have interactive behaviors with the member user C, and the member user B is used as a bridge between the initiating user A and the member user C, a certain relationship intimacy exists between the initiating user A and the member user C; because there is no interaction between the user members E, F and the initiating user A and the member users B, F, there is no affinity between the initiating user A and the user members E, F. The user interaction affinity reflects short-term and direct affinity relationship between the initiating user and the target group, and means the frequency of interaction between the initiating user and member users in the target group; group activity refers to the activity level of the initiating user in the target group.
The user interaction behavior of the user relationship affinity and/or the user interaction affinity may be at least one of a speaking behavior, a document sharing behavior, an online conference behavior, an offline conference invitation behavior, and the like. The speaking behavior may be a voice or text recording entered by the user in the group; the shared document behavior can be various document data records transmitted by the user in the group; the online conference behavior can be that users initiate online conference recording through instant messaging software in a group; the offline meeting invitation behavior may be an invitation record of an offline meeting initiated by the user at a particular time and place in the group, such as a meeting with all members of a department in a second meeting room at 3 pm, and the like.
S130, according to the sorting influence data, sorting each target group, and responding to the search request.
Specifically, the real search requirements of the initiating user on the groups can be reflected individually or in combination by aiming at the various sequencing influence data, and then a plurality of target groups are sequenced and displayed to the initiating user, so that the groups with the highest possibility of real requirements of the users are sequenced in front for the users to conveniently see and select.
There are various strategies for ranking the target groups based on the ranking influence data, for example, weighted summation may be specifically performed according to user relationship affinity, user interaction affinity, and group liveness of the target groups; and sorting according to the summation result of each target group, and displaying each target group according to the sorting result so as to respond to the search request. The weighted value in the weighted summation is determined according to the actual scene to which the instant messaging software belongs and the scene condition. For example, if the instant messaging software belongs to an enterprise office scenario or a school community scenario, the weighted values of the different ordering impact data may be set differently.
It can be understood that the sequencing result of the target group is displayed according to the sequencing influence data, so that the initiating user can be positioned to the group more quickly and accurately, the time is saved, and the searching experience of the user is improved.
Optionally, the target groups are sorted according to any sorting influence data of the user relationship affinity, the user interaction affinity and the group liveness of the target groups, and each target group is displayed according to a sorting result so as to respond to the search request.
Optionally, weighted summation is performed according to any two sorting influence data of the user relationship affinity, the user interaction affinity and the group liveness of the target groups, sorting is performed according to a summation result of each target group, and each target group is displayed according to a sorting result so as to respond to the search request. Wherein the weight value in the weighted summation is determined according to the experience value of the person skilled in the art.
Still alternatively, the three sort order influence data may be considered in parallel, or may be considered in series in a different order. For example, the target groups are sorted by user affinity and then sorted based on group liveness. The specific ordering impact policy is not limited.
The group set is searched according to the search condition in the search request, so that a plurality of target groups serving as search results are determined; determining at least one ordering impact data for the target group; wherein the ordering impact data comprises at least one of: the user relationship affinity between the initiating user of the search request and the member users in the target group, the user interaction affinity between the initiating user and the member users, and the group liveness of the target group; and after sequencing each target group according to the sequencing influence data, responding to the search request, optimizing the group search function in the instant messaging software, meeting the retrieval requirement of the user on the group and improving the user experience.
On the basis of the foregoing embodiment, after responding to the search request, in an optional manner of this embodiment, a ranking result of the target groups is evaluated according to a click rate of a user on the target groups within a set preamble range. The foregoing ranking impact data is adjusted based on the evaluation and in combination with experience of one skilled in the art. Wherein setting the preamble range refers to the first few names of the ranking results presented to the initiating user. Specifically, based on a data statistics mode, the click rate of the top 5 target groups in the target group sorting result by the user who is on-line is used for evaluating, and if the click rate of the top 5 target groups in the target group sorting result in the statistical result is high, the method for sorting the target groups based on the sorting influence data is effective; if the click rate of the top 5 target groups of the target group sorting result in the statistical result is low, the method for sorting the target groups based on the sorting influence data is invalid.
In another optional manner of this embodiment, the ranking result of the target group may be evaluated according to the subjective index gsb (good, same, bad). The foregoing ranking impact data is adjusted based on the evaluation results, in conjunction with experience of those skilled in the art. Specifically, two pieces of data are prepared based on a manual statistics manner, one is a control group, namely a ranking result of a target group of an online or previous version model, the other is an experimental group, namely a ranking result of a target group of a current version model, and whether the ranking result of the experimental group is better (good), the same (same) or bad (bad) than the ranking result of the control group is evaluated manually, so that the effect of the method for ranking the target group based on the ranking influence data is evaluated.
It can be understood that through the evaluation of the sorting result of the target group, the sorting influence data can be continuously optimized, and then the retrieval requirement of the user can be more accurately met, so that the use experience of the user is improved.
Fig. 2 is a flowchart of a searching method for an instant messaging group according to the present embodiment; further optimization is performed based on the above embodiment, and may be combined with various optional technical solutions in the above embodiment.
Further, the "determining user relationship affinity of the target group" is optimized as: calculating user relationship affinity between the initiating user and each member user in each target group based on a user relationship affinity model aiming at each target group; wherein the user relationship affinity is used for characterizing static association relationships and/or dynamic interaction relationships between users.
As shown in fig. 2, the searching method for an instant messaging group provided in this embodiment includes:
and S210, searching the group set according to the search condition in the search request to determine a plurality of target groups serving as search results.
S220, determining at least one sequencing influence data of the target group; wherein the ordering impact data comprises at least one of: the user interaction affinity between the initiating user and the member users in the target group, and the group liveness of the target group.
Optionally, determining the user relationship affinity of the target group specifically includes: aiming at each target group, calculating user relationship intimacy between the initiating user and each member user in each target group based on a user relationship intimacy model; the user relationship affinity is used for representing a static association relationship and/or a dynamic interaction relationship between users.
The static association relationship between users refers to an inherent relationship between users, for example, refers to a relationship between members in an organization structure in an enterprise IM, such as at least one relationship among a department relationship, a superior-inferior relationship, a colleague relationship, and the like. For example, the affinity between member users belonging to the same department is high, the affinity between member users belonging to different departments is low, and these affinities are weighted and summed to serve as the user relationship affinity of the initiating user and the target group.
The dynamic interaction relationship between two users refers to an affinity relationship between the two users based on an interaction behavior, and may be, for example, the number of interaction days or the number of interaction times between the two users based on instant messaging software in a first set period range. The first set period range may be set according to needs or experience of a person skilled in the art, and is, for example, 180 days, 1 year, and the like.
The user relationship affinity model, which may be a machine learning model or a function model, is capable of calculating a relationship affinity between users based on two or more users. For example, by inputting the user identification or user portrait characteristics into the user relationship affinity model, the relationship affinity values between users may be output or may be used to calculate relationship affinity. The user relationship affinity model may learn training based on static and/or dynamic relationships between users.
Specifically, a user relationship intimacy model can be generated according to weighted undirected graph training representing user relationships;
the user relationship intimacy model is used for generating a user relationship vector according to user mapping, and the user relationship vector is used for calculating a distance value representing the user relationship intimacy; each node of the weighted undirected graph corresponds to each user, and the weight between the nodes in the weighted undirected graph corresponds to the user relationship between two users.
Specifically, a user relationship vector is generated by using a user relationship affinity model, and an unquantizable user relationship is converted into a quantifiable user relationship vector. Firstly, constructing a weighted undirected graph representing the user relationship, wherein each node of the weighted undirected graph corresponds to each user and is marked as x; the edge weight between nodes in the weighted undirected graph represents the user relationship between two users, for example, the weight between nodes of the weighted undirected graph can be the number of interaction days or the number of interactions between two users.
Mapping each user x into a high-dimensional vector y, namely a user relation vector, through a user intimacy model, namely a mapping function f, and recording as: f: x- > y. The mapping function f has some unknown parameters, and the unknown parameters are continuously optimized through a weighted undirected graph to obtain an optimal user intimacy model. Specifically, assuming that a weighted undirected graph has nodes x1 and x2, \8230, starting from x1, inputting x1 into a user intimacy model to obtain a relationship vector y1, and inputting x2 into the user intimacy model to obtain a relationship vector y2; and calculating a relation vector y1 'of the node x1 and other nodes based on the weighted undirected graph, calculating a relation vector y2' between the node x2 and other nodes based on the weighted undirected graph, comparing whether y1 is equal to y1 'and whether y2 is equal to y2', and continuously adjusting parameters of a mapping function f in the user intimacy model until y1 is equal to y1 'or within an allowable error, and y2 is equal to y2' or within an allowable error to obtain an optimal user intimacy model. The user relationship intimacy model can be trained and learned based on a random walk unsupervised graph model algorithm Node2 Vec.
Illustratively, for each target group, inputting the identification of the initiating user and each member user in the target group into a user relationship affinity model to generate a user relationship vector through mapping; calculating an average relationship vector of the user relationship vectors of all the member users; and calculating a cosine distance value between the average relation vector and the user relation vector of the initiating user as the user relation intimacy between the initiating user and the target group.
The identifier may be at least one of a user name, a user ID, and the like of the user, and the user relationship vector is a high-dimensional vector of a high-dimensional space.
Specifically, aiming at each target group, the identification of an initiating user and each member user in the target group is input into a user relationship intimacy model, and a user relationship vector is generated through a mapping function in the user intimacy model; aggregating the relationship vectors of all member users, and dividing the aggregated relationship vectors by the total number of the member users in the target group to obtain an average relationship vector; and calculating a cosine distance value between the average relation vector and the user relation vector of the initiating user as the user relation intimacy between the initiating user and the target group. The larger the cosine distance value between the average relationship vector and the user relationship vector of the initiating user is, the closer the distance between the user relationship vector of the initiating user and the average relationship vector is, that is, the greater the user relationship intimacy between the initiating user and the target group is; the smaller the cosine distance value between the average relationship vector and the user relationship vector of the initiating user, the farther the distance between the user relationship vector of the initiating user and the average relationship vector is, that is, the smaller the affinity of the user relationship between the initiating user and the target group is.
And S230, according to the sorting influence data, after sorting the target groups, responding to the search request.
Specifically, the target groups are sorted according to the user relationship affinity between the initiating user and the target groups, and then the search request of the initiating user is responded.
According to the method and the device, the user relationship intimacy model is generated through training according to the weighted undirected graph representing the user relationship, the non-quantifiable user and user relationship and the like are converted into the quantifiable user and user relationship by the weighted undirected graph, the user relationship intimacy model is convenient to generate, the user relationship intimacy between the initiating user and each member user in each target group is calculated based on the user relationship intimacy model, and then the user relationship intimacy between the initiating user and the target group is determined. The user relationship affinity is introduced, so that the relationship between the users can be more conveniently represented, the target group of the initiating user can be more accurately searched, and the user experience is improved.
Fig. 3 is a flowchart of a searching method for an instant messaging group according to the present embodiment; further optimization is performed based on the above embodiment, and may be combined with various optional technical solutions in the above embodiment.
Further, optimizing the user interaction affinity of the determined target group to be specific to each target group, and determining the user interaction affinity between the initiating user and each member user according to the historical interaction behaviors of the initiating user and each member user in the target group; and determining the user interaction affinity between the initiating user and the target group according to the user interaction affinity between the initiating user and each member user in the target group.
As shown in fig. 3, the searching method for an instant messaging group provided in this embodiment includes:
and S310, searching the group set according to the search condition in the search request to determine a plurality of target groups serving as search results.
S320, determining at least one sequencing influence data of the target group; wherein the ordering impact data comprises at least one of: the user relationship affinity of the initiating user and the member users in the target group, the user interaction affinity between the initiating user and the member users and the group activity of the target group.
Optionally, determining the user interaction affinity of the target group specifically includes: aiming at each target group, determining the user interaction affinity between an initiating user and each member user according to the historical interaction behavior of the initiating user and each member user in the target group; and determining the user interaction intimacy between the initiating user and the target group according to the user interaction intimacy between the initiating user and each member user in the target group.
Illustratively, for each target group, the user interaction affinity between the initiating user and each member user in the target group is determined according to the historical interaction behaviors of the initiating user and each member user in the target group. Specifically, for each member user in each target group, acquiring the number of interaction days or the number of interaction times of the initiating user and the member users based on the instant messaging software in a second set period range as interaction data; based on the interactive data between the initiating user and other interactive users, carrying out normalization processing on the interactive data between the initiating user and the member users to form normalized interactive data; and performing time attenuation processing on the normalized interactive data by taking the time unit in the second set period range as granularity, and taking the attenuated normalized interactive data as the user interaction intimacy between the initiating user and the member user.
Wherein, the second setting period range can be set according to the needs or the experience value of the person skilled in the art.
Firstly, aiming at each user member in each target group, acquiring the number of interaction days or the number of interaction times of an initiating user and a member user based on instant messaging software in a second set period range as interaction data x, x = { x1, x2, \8230;, xi, \8230 }, wherein i >1, xi represents the number of interaction days or the number of interaction times of the initiating user and the ith member user based on the instant messaging software in the second set period range; based on the interaction data x between the initiating user and other interaction users, normalization processing is carried out on the interaction data between the initiating user and the member users by using the following formula, and normalized interaction data Score is formed.
Wherein, score (xi) represents the interaction data between the initiating user and the user xi, max (x) represents the maximum interaction days or the maximum interaction times of the initiating user and the member user based on the instant messaging software in the second set period, min (x) represents the minimum interaction days or the minimum interaction times of the initiating user and the member user based on the instant messaging software in the second set period, and epsilon is a very small number, such as 0.00001, and the function is to prevent the denominator from being 0.
It can be understood that adverse effects caused by singular value data are eliminated through normalization processing, so that the obtained user interaction intimacy is closer to reality and can reflect the degree of relationship between the initiating user and the target group.
It should be noted that, for users whose interactive data do not satisfy the second set period range, the median in the interactive data among all the users is used for complementing.
Then, with the time unit in the second set period range as the granularity, performing time attenuation processing on the normalized interaction data Score (x), taking the attenuated normalized interaction data as the user interaction affinity S between the initiating user and the member user, and calculating the following formula:
where n denotes the number of unit times in the second set period range, m denotes the unit time in the mth second set period range, and Score (m) denotes the normalized interaction data up to the unit time in the mth second set period range.
For example, if the second set period range is one year, the unit time in the second set period range is month, and the month is granularity, n =12; score (6) represents normalized interaction data up to 6 months ago when m = 6.
It can be understood that the normalized interaction data is processed through time attenuation, so that the smaller the weight occupied by the normalized interaction data before the occurrence time of the interaction behavior of the initiating user is, the larger the weight occupied by the normalized interaction data when the occurrence time of the interaction behavior of the initiating user is the latest period of time is, the change of the interaction behavior of the initiating user along with the occurrence time of the interaction behavior can be well represented, and the user interaction intimacy between the initiating user and the target group can be more intuitively represented.
Illustratively, the user interaction affinity between the initiating user and the target group is determined according to the user interaction affinity between the initiating user and each member user in the target group. Specifically, for each target group, the user interaction affinity between the initiating user and each member user in the target group is calculated to be an average value, which is used as the user interaction affinity Sj between the initiating user and the target group.
Wherein Sj represents the user interaction affinity between the initiating user and the jth target group, S (xij) represents the user interaction affinity between the initiating user and the member users in the jth target group, nj represents the number of the user members in the jth target group, and j represents the jth target group.
S330, according to the sorting influence data, after sorting the target groups, responding to the search request.
Specifically, each target group is sorted according to the user interaction affinity between the initiating user and the target group, and then the search request of the initiating user is responded.
According to the method and the device, the interactive intimacy between the initiating user and each member user is determined according to the historical interactive behaviors of the initiating user and each member in the target group, so that the interactive intimacy between the initiating user and the target group is determined, the target groups are sequenced according to the interactive intimacy, and then the search request of the initiating user is responded. By introducing the user interaction affinity, the interaction condition between the users can be represented more conveniently, the target group of the initiating user can be searched more accurately, and the user experience is improved.
Fig. 4 is a flowchart illustrating a searching method for an instant messaging group according to the present embodiment; further optimization is performed based on the above embodiment, and may be combined with various optional technical solutions in the above embodiment.
Further, optimizing "determining the group activity of the target group" to "count at least one active behavior data of the initiating user in the target group; and calculating the group activity of the initiating user in the target group according to the active behavior data of the initiating user in the target group.
As shown in fig. 4, the searching method for an instant messaging group provided in this embodiment includes:
and S410, searching the group set according to the search condition in the search request to determine a plurality of target groups serving as search results.
S420, determining at least one sequencing influence data of the target group; wherein the ordering impact data comprises at least one of: the user relationship affinity of the initiating user and the member users in the target group, the user interaction affinity between the initiating user and the member users and the group activity of the target group.
Optionally, determining the group activity of the target group specifically includes: counting at least one active behavior data of an initiating user in a target group; and calculating the group activity of the initiating user in the target group according to the active behavior data of the initiating user in the target group.
Illustratively, at least one active behavior data of the initiating user in the target group is counted, and the active behavior data may be at least one of speaking time difference, speaking number, average speaking number, and the like of the initiating user in the target group, and specifically may be:
speaking time difference, namely the time difference between the latest speaking time of the initiating user in the target group and the current time;
speaking times, namely speaking times of the initiating user in at least one third set period range; wherein the third set period range includes one or more of 3 days, 7 days, 30 days, 60 days, and 365 days;
the average number of talks, i.e., the number of talks of the initiating user in the target group, is divided by the number of months the initiating user joined in the target group.
Illustratively, the group liveness of the initiating user in the target group is calculated according to the active behavior data of the initiating user in the target group. Specifically, according to the activity data of the initiating user in the target group, the group activity Softmax (x) of the initiating user in the target group is calculated by adopting the following formula:
where n is the set of all groups, i.e., n = { x 1 ,x 2 ,…x n And x represents a target group, and xi represents the ith active behavior data of the initiating user in the target group.
S430, according to the sorting influence data, after sorting the target groups, responding to the search request.
Specifically, according to the group activity of the initiating user in each target group, the target groups are sorted, and then the search request of the initiating user is responded.
According to the embodiment of the application, the active behavior data of the initiating user in the target group are counted, the group activity of the initiating user in the target group is calculated according to the active behavior data of the initiating user in the target group, all the target groups are sequenced according to the group activity, and then the searching request of the initiating user is responded. By introducing the group activity, the activity degree of the user in the target group can be represented more conveniently, the target group of the initiating user can be searched more accurately, and the user experience is improved.
The technical scheme of the embodiment of the application is particularly suitable for group search in enterprise-level IM. Generally, an enterprise-level IM searches a group through two domains of a group name and a group member, wherein 10 to 50 results are usually recalled for secondary sorting; multiple (10-100) target group results may be first recalled from both the group name and the group members via a user-entered query; and then, performing secondary sorting through the features of the sorting influence data and a scoring formula, and displaying the secondary sorting to the user.
Fig. 5 is a structural diagram of a searching apparatus for an instant messaging group according to the present embodiment; the present embodiment is applicable to all enterprise-level IM group search scenarios. The device can be realized in a software and/or hardware mode, and can be integrated in electronic equipment such as a server or a user terminal.
As shown in fig. 5, the searching apparatus for instant messaging group provided in this embodiment includes a target group determining module 510, a ranking influence data determining module 520, and a search request responding module 530, wherein:
a target group determining module 510, configured to search the group set according to the search condition in the search request to determine a plurality of target groups as search results;
a ranking impact data determination module 520 for determining at least one ranking impact data of the target group; wherein the ordering impact data comprises at least one of: the user relationship affinity of the initiating user and the member users in the target group, the user interaction affinity between the initiating user and the member users and the group activity of the target group of the search request;
and a search request responding module 530, configured to respond to the search request after ranking the target groups according to the ranking influence data.
Optionally, the apparatus further comprises:
and the sequencing result evaluation module is used for evaluating the sequencing result of the target group according to the click rate of the user on the target group in the set preorder range.
Optionally, the ranking-affecting-data determining module 520 includes:
the user relationship intimacy determining unit is used for calculating the user relationship intimacy between the initiating user and each member user in each target group based on the user relationship intimacy model aiming at each target group; the user relationship affinity is used for representing a static association relationship and/or a dynamic interaction relationship between users.
Further, the user relationship intimacy degree determining unit is also used for training and generating a user relationship intimacy degree model according to a weighted undirected graph representing the user relationship; the user relationship intimacy model is used for generating a user relationship vector according to user mapping, and the user relationship vector is used for calculating a distance value representing the user relationship intimacy; each node of the weighted undirected graph corresponds to each user, and the weight between the nodes in the weighted undirected graph corresponds to the user relationship between two users.
The dynamic interaction relationship between every two users is the interaction days or the interaction times based on the instant messaging software between the two users in the first set period range.
Further, the user relationship affinity determining unit is specifically configured to:
aiming at each target group, inputting the identification of the initiating user and each member user in the target group into a user relationship affinity model to generate a user relationship vector by mapping;
calculating an average relationship vector of the user relationship vectors of all member users;
and calculating a cosine distance value between the average relation vector and the user relation vector of the initiating user as the user relation intimacy between the initiating user and the target group.
Optionally, the ranking influence data determining module 520 further includes a first user interaction affinity determining unit and a second user interaction affinity determining unit, where:
the first user interaction intimacy determining unit is used for determining the user interaction intimacy between the initiating user and each member user in the target group according to the historical interaction behavior of the initiating user and each member user in the target group;
and the second user interaction intimacy determining unit is used for determining the user interaction intimacy between the initiating user and the target group according to the user interaction intimacy between the initiating user and each member user in the target group.
Further, the first user interaction affinity determining unit further includes an interaction data obtaining subunit, a normalized interaction data forming subunit, and a first user interaction affinity determining subunit, where:
the interactive data acquisition subunit is used for acquiring the number of interactive days or interactive times of the initiating user and the member users based on the instant messaging software in a second set period range as interactive data aiming at each member user in each target group;
the normalized interactive data forming subunit is used for carrying out normalization processing on the interactive data between the initiating user and the member users based on the interactive data between the initiating user and other interactive users to form normalized interactive data;
and the first user interaction intimacy degree determining subunit is used for performing time attenuation processing on the normalized interaction data by taking the time unit in the second set period range as the granularity, and taking the attenuated normalized interaction data as the user interaction intimacy degree between the initiating user and the member user.
Further, the second user interaction affinity determining unit is specifically configured to:
and aiming at each target group, calculating the average value of the user interaction affinities between the initiating user and each member user in the target group, and taking the average value as the user interaction affinities between the initiating user and the target group.
Optionally, the ranking-affecting-data determining module 520 further includes: a group liveness determination unit comprising: an active behavior data acquisition subunit and a group activity determination subunit, wherein:
the active behavior data acquisition subunit is used for counting at least one active behavior data of the initiating user in the target group;
and the group activity determining subunit is used for calculating the group activity of the initiating user in the target group according to the active behavior data of the initiating user in the target group.
Further, the active behavior data includes at least one of:
initiating a time difference between the latest speaking time of the user in the target group and the current time;
initiating the number of times of speaking of the user in at least one third set period range; wherein the third set period range includes one or more of 3 days, 7 days, 30 days, 60 days, and 365 days;
the number of all utterances by the initiating user in the target group is divided by the number of months the initiating user joined the target group.
Further, the group activity determining subunit is specifically configured to:
according to the activity data of the initiating user in the target group, calculating the group activity Softmax (x) of the initiating user in the target group by adopting the following formula:
wherein x represents a target group, xi represents the ith active behavior data of the initiating user in the target group.
Optionally, the search request response module 530 is specifically configured to:
carrying out weighted summation according to the user relationship affinity, the user interaction affinity and the group activity of the target group;
and sorting according to the summation result of each target group, and displaying each target group according to the sorting result so as to respond to the search request.
Optionally, the user interaction behavior that represents the user interaction affinity includes at least one of the following:
a speech behavior, a shared document behavior, an online meeting behavior, and an offline meeting invitation behavior.
The searching device of the instant messaging group provided by the embodiment of the application can execute any searching method of the instant messaging group provided by the embodiment of the application, and has corresponding functional modules and beneficial effects of the executing method. Reference may be made to the description of any method embodiment of the present application for details not explicitly described in this embodiment.
According to an embodiment of the present application, an electronic device and a readable storage medium are also provided.
Fig. 6 is a block diagram of an electronic device according to an embodiment of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 6, the electronic apparatus includes: one or more processors 610, memory 620, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing some of the necessary operations (e.g., as an array of servers, a group of blade servers, or a multi-processor system). One processor 610 is illustrated in fig. 6.
The memory 620, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the instant messaging group search method in the embodiments of the present application (e.g., the target group determination module 510, the ranking impact data determination module 520, and the search request response module 530 shown in fig. X). The processor 610 executes various functional applications of the server and data processing by running non-transitory software programs, instructions, and modules stored in the memory 620, that is, implements the instant messaging group search method in the above-described method embodiments.
The memory 620 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created from use of the electronic device according to a search of the instant messenger group, and the like. Further, the memory 620 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 620 optionally includes memory located remotely from processor 610, which may be connected to the searching electronic device of the instant messaging group via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device of the searching method for the instant messaging group may further include: an input device 630 and an output device 640. The processor 610, the memory 620, the input device 630, and the output device 640 may be connected by a bus or other means, such as the bus connection in fig. 6.
The input device 630 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the electronic device for search of the instant messaging group, such as an input device of a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointing stick, one or more mouse buttons, a track ball, a joystick, etc. The output device 640 may include a display device, an auxiliary lighting device (e.g., an LED), a haptic feedback device (e.g., a vibration motor), and the like. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), the internet, and blockchain networks.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
According to the technical scheme of the embodiment of the application, the group set is searched according to the search condition in the search request, so that a plurality of target groups serving as search results are determined; determining at least one ordering impact data for the target group; wherein the ordering impact data comprises at least one of: the user relationship affinity of an initiating user of the search request and member users in a target group, the user interaction affinity between the initiating user and the member users, and the group activity of the target group; and after sequencing the target groups according to the sequencing influence data, responding to the search request, optimizing the group search function in the instant messaging software, meeting the retrieval requirement of the user on the groups and improving the user experience.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.
The above-described embodiments are not intended to limit the scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (7)
1. A searching method of instant communication group includes:
searching the group set according to the search condition in the search request to determine a plurality of target groups serving as search results;
determining ordering impact data of the target group; wherein the ranking impact data comprises user interaction affinities between the initiating user and the member users; wherein determining the user interaction affinity of the target group comprises:
acquiring the number of interaction days or the number of interaction times of the initiating user and the member users based on instant messaging software in a second set period range as interaction data aiming at each member user in each target group;
based on the interactive data between the initiating user and other interactive users, carrying out normalization processing on the interactive data between the initiating user and the member users to form normalized interactive data;
performing time attenuation processing on the normalized interaction data by taking the time unit in the second set period range as granularity, and taking the attenuated normalized interaction data as the user interaction intimacy between the initiating user and the member user;
determining user interaction affinity between the initiating user and the target group according to the user interaction affinity between the initiating user and each member user in the target group;
and responding to the search request after sequencing each target group according to the sequencing influence data.
2. The method of claim 1, wherein determining a user interaction affinity between the initiating user and the target group based on the user interaction affinities between the initiating user and each member user of the target group comprises:
and aiming at each target group, calculating the average value of the user interaction affinities between the initiating user and each member user in the target group as the user interaction affinities between the initiating user and the target group.
3. The method of claim 1, wherein after responding to the search request, further comprising:
and evaluating the sequencing result of the target group according to the click rate of the user on the target group in the set preamble range.
4. The method of claim 1, wherein the user-interaction behavior that embodies the user-interaction affinity comprises at least one of:
a speech behavior, a shared document behavior, an online meeting behavior, and an offline meeting invitation behavior.
5. A searching device for an instant communication group is disclosed,
the target group determining module is used for searching the group set according to the searching condition in the searching request so as to determine a plurality of target groups serving as searching results;
the sequencing influence data determining module is used for determining the sequencing influence data of the target group; wherein the ranking impact data comprises user interaction affinities between the initiating user and the member users;
wherein the sequencing influence data determining module comprises a first user interaction intimacy determining unit and a second user interaction intimacy determining unit, the first user interaction intimacy determining unit comprises an interaction data obtaining subunit, a normalized interaction data forming subunit and a first user interaction intimacy determining subunit, wherein,
the interactive data acquisition subunit is configured to acquire, as interactive data, the number of interactive days or the number of interactive times of the initiating user and the member users based on the instant messaging software within a second set period range for each member user in each target group;
the normalized interactive data forming subunit is used for carrying out normalization processing on the interactive data between the initiating user and the member users based on the interactive data between the initiating user and other interactive users to form normalized interactive data;
the first user interaction affinity determining subunit is configured to perform time attenuation processing on the normalized interaction data with a time unit in the second set period range as a granularity, and use the attenuated normalized interaction data as the user interaction affinity between the initiating user and the member user;
the second user interaction affinity determining unit is configured to determine, according to user interaction affinities between the initiating user and each member user in the target group, a user interaction affinity between the initiating user and the target group;
and the search request response module is used for responding the search request after sequencing each target group according to the sequencing influence data.
6. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the instant messaging group search method of any one of claims 1-4.
7. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the instant messenger group search method of any one of claims 1 to 4.
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| CN113051485B (en) * | 2021-03-26 | 2023-08-22 | 北京达佳互联信息技术有限公司 | Group searching method, device, terminal and storage medium |
| CN113254766A (en) * | 2021-05-20 | 2021-08-13 | 北京百度网讯科技有限公司 | Information retrieval method and device |
| CN113077194B (en) * | 2021-06-04 | 2022-05-17 | 北京明略昭辉科技有限公司 | Method and device for generating activity ranks |
| CN113572679B (en) * | 2021-06-30 | 2023-04-07 | 北京百度网讯科技有限公司 | Account intimacy generation method and device, electronic equipment and storage medium |
| CN113595874B (en) * | 2021-07-09 | 2023-03-24 | 北京百度网讯科技有限公司 | Instant messaging group searching method and device, electronic equipment and storage medium |
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