WO2018227773A1 - Procédé et appareil de recommandation de lieu, dispositif informatique et support de stockage - Google Patents

Procédé et appareil de recommandation de lieu, dispositif informatique et support de stockage Download PDF

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WO2018227773A1
WO2018227773A1 PCT/CN2017/099735 CN2017099735W WO2018227773A1 WO 2018227773 A1 WO2018227773 A1 WO 2018227773A1 CN 2017099735 W CN2017099735 W CN 2017099735W WO 2018227773 A1 WO2018227773 A1 WO 2018227773A1
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user
check
location
recommended
query
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PCT/CN2017/099735
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English (en)
Chinese (zh)
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王健宗
黄章成
吴天博
肖京
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平安科技（深圳）有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/01—Social networking

Definitions

  • personalized recommendations can help users filter out information that users are not interested in from rich and cumbersome data, and better discover user preferences to increase user activity in social networks.
  • the traditional personalized location recommendation method mostly analyzes the user's historical trajectory data, obtains the user's location preference, and then recommends the location similar to the preference to the user.
  • the location recommendation method based on the user's own trajectory data has the following drawbacks: First, since the amount of data of the user's own trajectory is small and single, the recommended location is relatively simple. Secondly, there may be places in the user history track data that the user does not like. Therefore, personalized recommendation based on the user's own historical track data cannot ensure accurate user preference.
  • a location recommendation method is provided.
  • a location recommendation method comprising:
  • a location recommending device comprising:
  • a request receiving module configured to receive a location recommendation request sent by the user terminal, where the location recommendation request carries the query user identifier
  • the check-in data search module is configured to search for the check-in data of the query user corresponding to the query user identifier, where the location social network platform generates the check-in data set according to the historical check-in behavior of the user, and the check-in data of each user in the check-in data set All include check-in locations;
  • An associated user determining module configured to search for an associated user of the querying user in the check-in data set, where the associated user has at least one check-in place and a check-in place of the querying user;
  • a similar user set determining module configured to calculate a similarity between the query user and each of the associated users, and determine a similar user set corresponding to the query user according to the calculated similarity
  • a check-in place collection determining module configured to determine a check-in place set corresponding to the similar user set, where the check-in place set includes a check-in place signed by all related users in the similar user set;
  • a recommended location determining module configured to remove the check-in location in the set of check-in locations that coincides with the querying user, obtain a set of recommended locations, and push the recommended location included in the set of recommended locations to the querying user terminal.
  • a computer apparatus comprising a memory and a processor, the memory storing computer executable instructions, the instructions being executed by the processor, causing the processor to perform the following steps:
  • the check-in data includes the check-in location
  • One or more non-volatile readable storage media storing computer-executable instructions, the instructions being executed by one or more processors, such that the one or more processors perform the following steps:
  • 1 is an application environment diagram of a location recommendation method in an embodiment
  • FIG. 2 is a flow chart of a method for recommending a place in an embodiment
  • FIG. 3 is a flowchart of calculating a similarity between a query user and an associated user in an embodiment
  • Figure 4 is a flow chart involved in the location recommendation step in one embodiment
  • Figure 5 is a block diagram showing the structure of a location recommendation device in an embodiment
  • FIG. 6 is a schematic diagram showing the internal structure of a server in an embodiment.
  • an application environment diagram of a location recommendation method including an inquiry terminal 110 and a server 120.
  • the query terminal 110 can communicate with the server 120 over a network.
  • the query terminal 110 may be at least one of a smartphone, a tablet, a notebook, and a desktop computer, but is not limited thereto.
  • the server 120 is a server or a server cluster of a Location-based Social Network (LBSN), and the user's check-in data is stored in the database.
  • LBSN Location-based Social Network
  • the server 120 receives the location recommendation request sent by the user terminal, searches for the check-in data of the query user from the check-in database according to the carried query user identifier, and then searches for the associated user that has the overlapping check-in location with the query user from the check-in database.
  • the server 120 calculates the similarity between the query user and the associated user according to the check-in data of the query user and the check-in data of the associated user, and determines the associated user whose similarity meets the set condition as the member of the similar user set corresponding to the query user.
  • the members of the similar user set have similar location preferences as the querying user, and the check-in place of the similar user centralized member can better fit the querying user's preference, and the check-in location is relative to the querying user's history. Points have a certain diversity.
  • FIG. 2 is a schematic flow chart of a location recommendation method according to an embodiment of the present invention. It should be understood that although the various steps in the flowchart of FIG. 2 are sequentially displayed as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Except as explicitly stated herein, the execution of these steps is not strictly limited, and may be performed in other sequences. Moreover, at least some of the steps in FIG. 2 may include a plurality of sub-steps or stages, which are not necessarily performed sequentially, but may be alternated or alternated with at least a part of other steps or sub-steps or stages of other steps. carried out.
  • a location recommendation method is provided.
  • the method is illustrated in the server 120 in FIG. 1, and specifically includes the following steps:
  • Step S202 Receive a location recommendation request sent by the user terminal, where the location recommendation request carries the query user identifier.
  • the terminal device that can communicate with the location social network platform server is installed in the query user terminal, and the user terminal is queried to send a location recommendation request to the server through the terminal application.
  • the recommendation request can be sent by clicking on the "Location Recommendations" button in the application interface.
  • the user After the terminal logs in to the server, the user sends a recommendation request to the server by using a regular shaking terminal body.
  • the server receives the user login platform request, it is deemed to query the terminal where the user is located to send a location recommendation request to the server, that is, the server performs location recommendation for each logged-in user.
  • the above location social networking platform can be Foursquare, Gowalla or Facebook Places.
  • the user's check-in time, check-in location, and evaluation content made to the location are included.
  • the evaluation content made by the user on the location may include an evaluation in a text form, and may also include a rating evaluation.
  • Step S204 Search for the check-in data of the query user corresponding to the query user identifier, where the location social network platform generates the check-in data set according to the historical check-in behavior of the user, and the check-in data of each user in the check-in data set includes the check-in place.
  • the server searches for the corresponding check-in data in the check-in data set according to the user ID of the querying user.
  • the check-in data set is a set of check-in data generated according to the check-in behavior of the user in the platform, and the user's check-in behavior is a check-in request sent to the server at a certain place during the historical time period.
  • the check-in request carries the user's evaluation information about the check-in location.
  • the server generates the check-in data according to the check-in behavior of the user, including: obtaining the location information of the user when the check-in is located, and locating the check-in place according to the location information, such as a restaurant, a tourist scenic spot, and the like.
  • the check-in data further includes generating numerical rating information for the check-in location based on the evaluation information entered by the user.
  • step S206 the associated user of the querying user is searched in the check-in data set, and the associated user has at least one check-in place coincident with the check-in place of the querying user.
  • the server finds whether there is a check-in data that coincides with the check-in location of the query user in the check-in data set. If yes, the user corresponding to the checked-in data is defined as the associated user of the query user.
  • the associated user and the querying user may have a check-in place coincident, or multiple check-in locations may coincide.
  • the check-in data of the query user u is: a, b, c
  • the check-in data of the user v is: a, d, e
  • the check-in data of the user w is: b, a, f,.
  • the user v has a location a that has been visited by the query user u. Therefore, the user v is the associated user who queries the user u.
  • the user w and the query user u have two locations that have been visited together, respectively b and a. Therefore, the user w is an associated user who queries the user u.
  • Step S208 Calculate the similarity between the query user and each associated user, and determine a similar user set corresponding to the query user according to the calculated similarity.
  • the associated user of the querying user determined according to step S206 may be a large number of user groups.
  • this step filters the determined associated users, and determines and selects the user's location preferences from the associated users.
  • a similar group of users that is, a set of similar users that determine the querying user.
  • the specific method for determining the similar user set of the querying user is: calculating the similarity between the two according to the check-in data of the querying user and the associated user, and selecting the sorting according to the order of similarity in the order of similarity.
  • the associated user of the previous set number of similarities is the similar user set of the querying user. That is, the associated users corresponding to the first N largest similarities are selected as the similar user set of the querying user.
  • Step S210 Determine a set of check-in locations corresponding to the set of similar users, where the set of check-in locations includes A similar user collects the check-in locations that all associated users have checked in.
  • Step S212 Remove the check-in place that coincides with the query user in the set of check-in places, obtain a set of recommended places, and push the recommended place included in the set of recommended places to the query user terminal.
  • the check-in location set corresponding to the similar user set is determined according to the check-in data of all associated users included in the similar user set. That is, the check-in locations visited by all associated users in a similar user set can be found in the corresponding check-in place set.
  • the associated users in a similar user set are: associated users A: a, d, e; associated users B: b, a, f; and associated users C: c, b, e, then the similar user set
  • the corresponding check-in place set is ⁇ a, b, c, d, e, f ⁇ .
  • the check-in place where the user has checked in is removed from the check-in place set corresponding to the similar user set, and the recommended place set is obtained. If the check-in location set of the query user is ⁇ a, b, c), the recommended place set is ⁇ d, e, f ⁇ , and the recommended place in the recommended place set is pushed to the query user terminal.
  • the user in the similar user set of the query user has a similar location preference to the query user, and the location visited by the user in the similar user set can fit the query user's preference with a certain probability.
  • the sign-in location corresponding to the similar user set is used as the basis of the recommended location, and the diversity of the users determines that the recommended location is also more diverse.
  • the recommended location is not limited to the same type of location that queries the location visited by the user itself, but may be to some extent fit other types of locations that query the user's preferences.
  • the check-in data further includes generating numerical rating information for the check-in location based on the rating information entered by the user.
  • the check-in data of the querying user may include (a, 0.8), (b, 0.5), (c, 0.3) 3 check-in data.
  • the a, b, and c in the check-in data are the check-in locations of the query user, and the value in each data is the score of the query user for the check-in place.
  • Each check-in data of the query user corresponds to a specific check-in time. When the query user signs in to a check-in location at a different check-in time, multiple check-in data will be generated. If the user is checked in at time t 1 and t 2, the user is checked in at location a. Two check-in data will be generated, such as (a, 0.8), (a, 0.9).
  • step S208 calculating a similarity between the query user and each associated user, and determining a similar user set corresponding to the query user according to the calculated similarity, including the following steps:
  • Step S302 Calculate the similarity between each associated user and the querying user, wherein the querying user The more concentrated the check-in places with the associated users and the closer the scores to the common check-in places, the larger the calculated similarity value.
  • the determination of whether the check-in location of the user and the associated user is centralized depends on the following factors: the sum of the times of signing in to the same place (considering the case where one of the users signs in to the common check-in place multiple times) and the sum of the times of signing in the non-shared place. The greater the sum of the times of signing in to the same place and the smaller the total number of times of signing in the non-co-signing place, the more concentrated the check-in place of the associated user and the querying user.
  • the set of the check-in location of the query user is ⁇ a, b, c, a, d, e, f, e, h ⁇
  • the set of the check-in locations of the associated user A is ⁇ a, m, i, l, m, b, f, k, h ⁇
  • the collection of the location of the associated user B is ⁇ a, b, e, o, f, k, m ⁇
  • the total number of times that the associated user A and the querying user check in to the same place is 5, which are a, a, b, f, and h, and the number of times of signing in the non-shared place is 9, c, d, e, e, m, i, respectively. l, m, k; the total number of times that the associated user B and the querying user check in to the same place are 6, respectively, a, b, a, e, e, f, and the number of times of signing in the non-shared place is 6, respectively, c, d, h, o, m, k. It can be seen from the above analysis that the location of the check-in between the associated user B and the querying user is more concentrated.
  • whether the scores of the common check-in places are relatively close can be determined by calculating the variance value or the standard deviation or the absolute value of the score difference of the scores of the querying user and the associated user for the same check-in place.
  • the absolute value of the score difference as an example, for example, the locations of the query user and the associated user are a and b respectively, and the scores of the two are ⁇ 0.6, 0.8 ⁇ , ⁇ 0.5, 0.9 ⁇ , respectively, and the scores of the two are respectively
  • the closeness is
  • Step S304 The related users whose similarity is greater than the set threshold are grouped into a similar user set of the querying user.
  • the similarity between the query user and the associated user is calculated, and the associated user whose similarity is greater than the set threshold is regarded as a member of the similar user set of the query user.
  • the size of the threshold may be preset, for example, may be 0.8. Associated users with a similarity greater than 0.8 constitute a similar set of users for the querying user. If the size of the similar user set member determined according to the preset threshold is small (for example, if the determined number of related users in the similar user set is less than 2), the size of the set threshold is adjusted to re-determine the similar user set.
  • the server presets a plurality of levels of similarity thresholds, such as an accurate threshold (eg, 0.8), standard threshold (eg 0.6), extensive threshold (eg 0.4), precise threshold > standard threshold > extensive threshold.
  • an accurate threshold eg, 0.8
  • standard threshold eg 0.6
  • extensive threshold eg 0.4
  • the calculated similarity can more reflect the degree of similarity between the associated user and the querying user, so that the recommendation of the location according to the similar user can be more appropriately posted. User's preference for location.
  • the similarity between each associated user and the querying user can be calculated by the following formula:
  • u and v represent the query user and the associated user respectively;
  • sim(u, v) is the similarity between the associated user and the querying user;
  • R ui and R vi are the scores of the query user and the associated user respectively for the location i;
  • r j is the number of times the query user or the associated user checks the location j;
  • r max is the location social
  • the number of times of sign-in corresponding to the sign-in location with the most sign-on by any user in the network platform is substantially a fixed value for the social network platform r max at the same location, and its role is to perform normalization of the score.
  • the similarity calculated by equation (1) not only measures the set of places that two users have visited together. Moreover, the location of other non-common visits, that is, the degree of dispersion (or concentration) of the two places of visit, and the consideration of the user's scoring factors on the place of check-in are considered, so that the similarity of the calculation can accurately evaluate the two. Whether the location preferences are similar.
  • step S210 in addition to the check-in location in the set of check-in locations that coincides with the querying user, obtaining a set of recommended locations, and pushing the recommended locations included in the set of recommended locations to the querying user terminal include:
  • Step S402 Remove the check-in place that coincides with the query user in the set of check-in places, and obtain a set of places to be recommended.
  • the location where all the query users have not checked in in the similar user set is the set of places to be recommended.
  • the place recommended to the querying user should be a place where the user has not been visited.
  • the location to be recommended by the querying user is extracted from the set of check-in locations corresponding to the similar user set.
  • the associated users in the similar user set have certain location preference similarities with the query users.
  • the query based on the check-in locations corresponding to the similar user sets is recommended to the query user preferences to a certain extent.
  • Step S404 Calculate the interest degree of each of the to-be-recommended locations in the set of the to-be-recommended locations, and push the to-be-recommended locations with the interest degree greater than the set threshold to the querying user terminal; wherein the interest degree is collected by querying users and similar users. The similarity between the associated users who sign in to the recommended location and the rating of the associated user to the recommended location are calculated.
  • the determined location to be recommended is further accurately selected to select the location that best fits the true preference of the querying user. Specifically: calculating the degree of interest between the location to be recommended and the querying user. The higher the interest between the recommended location and the querying user, the higher the fit between the location and the querying user preferences.
  • the related users who have checked in to the recommended location are first searched in a similar user set. Then, according to the similarity between the searched related user and the querying user calculated in step S208 and the score of the associated user on the check-in place, the degree of interest of the querying user and the to-be-queried place is calculated. That is, the relationship between the user and the location is obtained by the similarity relationship between the associated user and the querying user and the rating relationship between the associated user and the location.
  • the degree of interest between the location A to be recommended and the querying user is calculated.
  • the related users who have visited the to-be-recommended location A in a similar user set are respectively associated users u 1 , u 2 , and u 3 .
  • the respective check-in data of the associated users u 1 , u 2 , and u 3 includes their rating information for the recommended location A, and the query user is calculated according to the calculated similarity between the associated user and the querying user and the score of the associated user to the recommended location A.
  • the degree of interest of the recommended location A wherein the higher the rating of the associated user to the recommended location, the higher the similarity between the associated user and the querying user, the higher the interest of the recommended location and the querying user.
  • the degree of interest between the querying user and the place to be recommended may be calculated by the following formula (2):
  • u is the querying user
  • j is the determined to-be-recommended location in the set of to-be-recommended locations
  • U is the similar user set of the querying user
  • u k is the associated user of the similarly-intended centralized sign-to-recommended location j
  • sim(u , u k ) is the similarity between the query user u and the associated user u k , The rating of the recommended location j for the associated user u k .
  • the formula (2) can calculate the degree of interest of the querying user and the place to be recommended by using the relationship between the querying user and the associated user and the location to be recommended, and the degree of interest can well reflect the degree of interest of the querying user in the recommended location. Pushing the to-be-recommended location with a greater degree of interest to the querying user terminal, so that the pushed location is more suitable for the user's own real preference, that is, accurate pushing for the user is realized.
  • a location recommendation device comprising:
  • the request receiving module 502 is configured to receive a location recommendation request sent by the query user terminal, and recommend the location.
  • the request carries the query user ID.
  • the check-in data search module 504 searches for the check-in data of the query user corresponding to the query user identifier, wherein the location social network platform generates the check-in data set according to the historical check-in behavior of the user, and the check-in data of each user in the check-in data set includes the check-in place.
  • the associated user determining module 506 searches for the associated user of the querying user in the check-in data set, and the associated user has at least one check-in place coincident with the check-in location of the querying user.
  • the similar user set determining module 508 is configured to calculate a similarity between the query user and each associated user, and determine a similar user set corresponding to the query user according to the calculated similarity.
  • the check-in place collection determining module 510 is configured to determine a check-in place set corresponding to the similar user set, where the check-in place set includes the check-in place signed by all the associated users in the similar user set.
  • the recommended location determining module 512 is configured to remove the check-in place in the set of check-in locations that coincides with the querying user, obtain a set of recommended locations, and push the recommended locations included in the set of recommended locations to the querying user terminal.
  • the check-in data further includes a rating of the check-in location;
  • the similar user set determining module 508 is further configured to calculate a similarity between each associated user and the querying user, wherein the querying user and the associated user are checked in. The more concentrated the check-in place is, the closer the score to the common check-in place is, the larger the similarity value is calculated; the related users whose similarity is greater than the set threshold constitute a similar user set of the query user.
  • the similarity between each associated user and the querying user is calculated by the following formula:
  • u and v represent the query user and the associated user respectively;
  • sim(u, v) is the similarity between the associated user and the querying user;
  • R ui and R vi are the scores of the query user and the associated user respectively for the location i;
  • r max is the location of the check-in location of the location social network platform that is most frequently signed by any user. The number of check-ins.
  • the recommended location determining module 512 is further configured to remove the check-in place in the set of check-in locations that coincides with the querying user, obtain a set of locations to be recommended, and calculate an interest of each of the to-be-recommended locations in the set of the querying user and the to-be-recommended location.
  • the degree to be recommended is pushed to the querying user terminal, where the degree of interest is the degree of similarity between the user who is in the recommended location by the query user and the similar user, and the associated user treats the recommendation.
  • the rating of the location is calculated.
  • the formula for calculating the degree of interest of the user and the place to be recommended is:
  • u is the querying user
  • j is the determined to-be-recommended location in the set of to-be-recommended locations
  • U is the similar user set of the querying user
  • u k is the associated user of the similarly-intended centralized sign-to-recommended location j
  • sim(u , u k ) is the similarity between the query user u and the associated user u k , The rating of the recommended location j for the associated user u k .
  • the data distribution apparatus in the various embodiments described above may be implemented in the form of a computing program, and the computer executable instructions corresponding to the computer program may be executed on a computer device as shown in FIG.
  • the computer device can be a physical server or a server cluster composed of multiple servers.
  • Its internal structure includes: a processor connected through a system bus, a non-volatile storage medium, an internal memory, and a network interface.
  • the non-volatile storage medium of the computer device stores an operating system, a database, and at least one of the above-described computer-executable instructions implemented by the data distribution apparatus.
  • the database is used to store data, such as storing user's check-in data.
  • the processor is used to provide computing and control capabilities to support the operation of the entire computer device.
  • the internal memory provides an environment for the operation of an operating system in a non-volatile storage medium and computer-executable instructions for implementing data distribution.
  • the network interface is used for communication connection with the query terminal.
  • FIG. 6 is only a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the server to which the solution of the present application is applied, and a specific server. More or fewer components than those shown in the figures may be included, or some components may be combined, or have different component arrangements.
  • the above network interface may be an Ethernet card or a wireless network card.
  • the above modules may also be embedded in hardware or independent of the computer device described above. It may also be stored in the memory of the differential distribution server in the form of software as described above, so that the processor calls to perform the operations corresponding to the above respective modules.
  • the processor can be a central processing unit (CPU), a microprocessor, a microcontroller, or the like.
  • one or more non-volatile readable storage media storing computer-executable instructions are provided, the instructions being executed by one or more processors, causing one or more processors to perform the All or part of the process in the embodiment method.
  • the computer executable instructions described above are computer executable instructions corresponding to a computer program implemented by all or part of the processes of the various embodiments described above.
  • the program can be stored in a computer readable storage medium, such as the present invention.
  • the program can be stored in a non-volatile readable storage medium of the computer system and executed by at least one processor in the computer system to implement a process comprising an embodiment of the methods described above.
  • the non-volatile readable storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or the like.

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Abstract

L'invention concerne un procédé de recommandation de lieu, comportant les étapes consistant à: recevoir une demande de recommandation de lieu émise par un terminal d'utilisateur d'interrogation, la demande de recommandation de lieu portant un identifiant d'utilisateur d'interrogation; rechercher des données d'ouverture de session d'un utilisateur d'interrogation correspondant à l'identifiant d'utilisateur d'interrogation, une plate-forme de réseau social de position générant un ensemble de données d'ouverture de session d'après des comportements passés d'ouverture de session d'utilisateurs, et les données d'ouverture de session de chaque utilisateur dans l'ensemble de données d'ouverture de session comportant un lieu d'ouverture de session; rechercher, dans l'ensemble de données d'ouverture de session, des utilisateurs associés de l'utilisateur d'interrogation; calculer la similarité entre l'utilisateur d'interrogation et chaque utilisateur associé, et déterminer un ensemble d'utilisateurs similaires correspondant à l'utilisateur d'interrogation d'après la similarité calculée; déterminer un ensemble de lieux d'ouverture de session correspondant à l'ensemble d'utilisateurs similaires; et éliminer le lieu d'ouverture de session coïncidant avec l'utilisateur d'interrogation dans l'ensemble de lieux d'ouverture de session pour obtenir un ensemble de lieux recommandés, et distribuer sélectivement des lieux recommandés compris dans l'ensemble de lieux recommandés au terminal d'utilisateur d'interrogation.
PCT/CN2017/099735 2017-06-12 2017-08-30 Procédé et appareil de recommandation de lieu, dispositif informatique et support de stockage WO2018227773A1 (fr)

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CN110059248B (zh) * 2019-03-21 2022-12-13 腾讯科技（深圳）有限公司 一种推荐方法、装置及服务器
CN110866180B (zh) * 2019-10-12 2022-07-29 平安国际智慧城市科技股份有限公司 资源推荐方法、服务器及存储介质
CN111523031B (zh) * 2020-04-22 2023-03-31 北京百度网讯科技有限公司 用于推荐兴趣点的方法和装置
CN111737537B (zh) * 2020-07-21 2020-11-27 杭州欧若数网科技有限公司 基于图数据库的poi推荐方法、设备及介质

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CN106056455A (zh) * 2016-06-02 2016-10-26 南京邮电大学 一种基于位置和社交关系的群组与地点推荐方法

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CN106056455A (zh) * 2016-06-02 2016-10-26 南京邮电大学 一种基于位置和社交关系的群组与地点推荐方法

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