WO2018227773A1 - Place recommendation method and apparatus, computer device, and storage medium - Google Patents
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- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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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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Claims (20)
- 一种地点推荐方法,所述方法包括:A location recommendation method, the method comprising:接收查询用户终端发送的地点推荐请求,所述地点推荐请求中携带查询用户标识;Receiving a location recommendation request sent by the user terminal, where the location recommendation request carries the query user identifier;查找与所述查询用户标识对应的查询用户的签到数据,其中,位置社交网络平台根据用户的历史签到行为生成签到数据集,所述签到数据集中每个用户的签到数据均包括签到地点;Searching for the check-in data of the query user corresponding to the query user identifier, where the location social network platform generates a 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 location;在所述签到数据集中查找所述查询用户的关联用户,所述关联用户至少有一个签到地点与所述查询用户的签到地点重合;Searching, in the check-in data set, an associated user of the query user, where the associated user has at least one check-in place and a check-in place of the query user;计算查询用户与每个所述关联用户之间的相似度,并根据计算的所述相似度确定所述查询用户对应的相似用户集;Calculating a similarity between the query user and each of the associated users, and determining a similar user set corresponding to the query user according to the calculated similarity;确定所述相似用户集所对应的签到地点集合,所述签到地点集合中包括所述相似用户集中所有关联用户所签到的签到地点;及Determining a set of check-in locations corresponding to the similar set of users, where the set of check-in locations includes a check-in place signed by all associated users in the similar user set; and去除所述签到地点集合中与所述查询用户重合的签到地点,得到推荐地点集合,将所述推荐地点集合中所包含的推荐地点推送至所述查询用户终端。And removing the check-in place in the set of check-in locations that coincides with the query user, obtaining a set of recommended places, and pushing the recommended place included in the set of recommended places to the querying user terminal.
- 根据权利要求1所述的方法,其特征在于,所述签到数据还包括对所述签到地点的评分;The method of claim 1 wherein said check-in data further comprises rating said check-in location;所述计算查询用户与每个所述关联用户之间的相似度,并根据计算的所述相似度确定所述查询用户对应的相似用户集,包括:The calculating the similarity between the user and each of the associated users, and determining the similar user set corresponding to the querying user according to the calculated similarity, including:计算每个所述关联用户与所述查询用户之间的相似度,其中,所述查询用户与所述关联用户所签到的签到地点越集中、对共同签到地点的评分越接近,计算得到的相似度数值越大;Calculating a similarity between each of the associated users and the querying user, wherein the closer the check-in place that the querying user and the associated user sign in, the closer the score to the common sign-in place, and the calculated similarity The greater the degree value;将所述相似度大于设定阈值的所述关联用户组成所述查询用户的相似用户集。The associated users whose similarities are greater than a set threshold are grouped into similar user sets of the querying users.
- 根据权利要求2所述的方法,其特征在于,所述计算每个所述关联用户与所述查询用户之间的相似度为:The method according to claim 2, wherein the calculating the similarity between each of the associated users and the querying user is:通过下述公式计算每个所述关联用户与所述查询用户之间的相似度: The similarity between each of the associated users and the querying user is calculated by the following formula:其中,u、v分别代表查询用户和关联用户;sim(u,v)为关联用户与查询用户之间的相似度;为关联用户与查询用户共同的签到地点;为关联用户与查询用户非共同的签到地点;Rui和Rvi分别为查询用户、关联用户对地点i的评分;rmax为位置社交网络平台中被任一用户签到次数最多的签到地点对应的签到次数。Where 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; A check-in location common to the associated user and the querying user; For the associated user and the query user, the check-in location is not common; 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.
- 根据权利要求2所述的方法,其特征在于,所述去除所述签到地点集合中与所述查询用户重合的签到地点,得到推荐地点集合,将所述推荐地点集合中所包含的推荐地点推送至所述查询用户终端,包括:The method according to claim 2, wherein the removing the check-in place in the set of check-in locations that coincides with the query user, obtaining a set of recommended places, and pushing the recommended place included in the set of recommended places The querying user terminal includes:去除所述签到地点集合中与所述查询用户重合的签到地点,得到待推荐地点集合;Removing a check-in place in the set of check-in locations that coincides with the query user, and obtaining a set of places to be recommended;计算所述查询用户与所述待推荐地点集合中每一个待推荐地点的兴趣度,将所述兴趣度大于设定阈值的所述待推荐地点推送至所述查询用户终端;Calculating the degree of interest of the to-be-recommended location in the set of the to-be-recommended locations, and pushing the to-be-recommended location with the interest degree greater than a set threshold to the querying user terminal;其中,所述兴趣度是通过所述查询用户与所述相似用户集中签到所述待推荐地点的关联用户之间的所述相似度以及所述关联用户对所述待推荐地点的评分计算得到的。The degree of interest is calculated by the similarity between the querying user and the related user of the similar user, and the rating of the to-be-recommended location by the associated user. .
- 根据权利要求4所述的方法,其特征在于,所述查询用户与所述待推荐地点的兴趣度的计算公式为:The method according to claim 4, wherein the formula for calculating the degree of interest of the querying user and the location to be recommended is:其中,u为查询用户,j为确定的待推荐地点集合中的待推荐地点;U为所述查询用户的相似用户集,uk是所述相似用户集中的签到过所述待推荐地点j的关联用户;sim(u,uk)为查询用户u与关联用户uk之间的相似度,为关联用户uk对待推荐地点j的评分。 Where u is the querying user, j is the determined to-be-recommended location in the set of to-be-recommended locations; U is a similar set of users of the querying user, and u k is the signing of the to-be-recommended location j of the similar user set Associated user; 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 .
- 一种地点推荐装置,其特征在于,所述装置包括:A location recommending device, the device comprising:请求接收模块,用于接收查询用户终端发送的地点推荐请求,所述地点推荐请求中携带查询用户标识;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;签到数据查找模块,用于查找与所述查询用户标识对应的查询用户的签到数据,其中,位置社交网络平台根据用户的历史签到行为生成签到数据集,所述签到数据集中每个用户的签到数据均包括签到地点;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;关联用户确定模块,用于在所述签到数据集中查找所述查询用户的关联用户,所述关联用户至少有一个签到地点与所述查询用户的签到地点重合;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;相似用户集确定模块,用于计算查询用户与每个所述关联用户之间的相似度,并根据计算的所述相似度确定所述查询用户对应的相似用户集;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;签到地点集合确定模块,用于确定所述相似用户集所对应的签到地点集合,所述签到地点集合中包括所述相似用户集中所有关联用户所签到的签到地点;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;推荐地点确定模块,用于去除所述签到地点集合中与所述查询用户重合的签到地点,得到推荐地点集合,将所述推荐地点集合中所包含的推荐地点推送至所述查询用户终端。And 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.
- 根据权利要求6所述的装置,其特征在于,所述签到数据还包括对所述签到地点的评分;所述相似用户集确定模块,还用于计算每个所述关联用户与所述查询用户之间的相似度,其中,所述查询用户与所述关联用户所签到的签到地点越集中、对共同签到地点的评分越接近,计算得到的相似度数值越大;将所述相似度大于设定阈值的所述关联用户组成所述查询用户的相似用户集。The device according to claim 6, wherein the check-in data further includes a score for the check-in place; the similar user set determining module is further configured to calculate each of the associated users and the query user The degree of similarity between the query user and the associated user is more concentrated, and the closer to the common check-in place, the greater the similarity value calculated; the similarity is greater than The associated users of the threshold constitute a similar set of users of the querying user.
- 根据权利要求7所述的装置,其特征在于,所述相似用户集确定模块,还用于通过下述公式计算每个所述关联用户与所述查询用户之间的相似度:The apparatus according to claim 7, wherein the similar user set determining module is further configured to calculate a similarity between each of the associated users and the querying user by using the following formula:其中,u、v分别代表查询用户和关联用户;sim(u,v)为关联用户与查询用户之间的相似度;为关联用户与查询用户共同的签到地点;为关联用户与查询用户非共同的签到地点;Rui和Rvi分别为查询用户、关联用户对 地点i的评分;rmax为位置社交网络平台中被任一用户签到次数最多的签到地点对应的签到次数。Where 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; A check-in location common to the associated user and the querying user; For the associated user and the query user, the check-in location is not common; 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.
- 根据权利要求7所述的装置,其特征在于,所述推荐地点确定模块,还用于去除所述签到地点集合中与所述查询用户重合的签到地点,得到待推荐地点集合;计算所述查询用户与所述待推荐地点集合中每一个待推荐地点的兴趣度,将所述兴趣度大于设定阈值的所述待推荐地点推送至所述查询用户终端;The device according to claim 7, wherein the recommended location determining module is further configured to remove a check-in place in the set of check-in locations that coincides with the query user, obtain a set of locations to be recommended, and calculate the query. And the interest to be recommended by the user and the to-be-recommended location in the set of to-be-recommended locations, and the to-be-recommended location with the interest degree greater than a set threshold is pushed to the querying user terminal;其中,所述兴趣度是通过所述查询用户与所述相似用户集中签到所述待推荐地点的关联用户之间的所述相似度以及所述关联用户对所述待推荐地点的评分计算得到的。The degree of interest is calculated by the similarity between the querying user and the related user of the similar user, and the rating of the to-be-recommended location by the associated user. .
- 根据权利要求9所述的装置,其特征在于,所述查询用户与所述待推荐地点的兴趣度的计算公式为:The device according to claim 9, wherein the formula for calculating the degree of interest of the querying user and the location to be recommended is:其中,u为查询用户,j为确定的待推荐地点集合中的待推荐地点;U为所述查询用户的相似用户集,uk是所述相似用户集中的签到过所述待推荐地点j的关联用户;sim(u,uk)为查询用户u与关联用户uk之间的相似度,为关联用户uk对待推荐地点j的评分。Where u is the querying user, j is the determined to-be-recommended location in the set of to-be-recommended locations; U is a similar set of users of the querying user, and u k is the signing of the to-be-recommended location j of the similar user set Associated user; 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 .
- 一种计算机设备,包括存储器和处理器,所述存储器中存储有计算机可执行指令,所述指令被所述处理器执行时,使得所述处理器执行以下步骤: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:接收查询用户终端发送的地点推荐请求,所述地点推荐请求中携带查询用户标识;Receiving a location recommendation request sent by the user terminal, where the location recommendation request carries the query user identifier;查找与所述查询用户标识对应的查询用户的签到数据,其中,位置社交网络平台根据用户的历史签到行为生成签到数据集,所述签到数据集中每个用户的签到数据均包括签到地点;Searching for the check-in data of the query user corresponding to the query user identifier, where the location social network platform generates a 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 location;在所述签到数据集中查找所述查询用户的关联用户,所述关联用户至少有一个签到地点与所述查询用户的签到地点重合; Searching, in the check-in data set, an associated user of the query user, where the associated user has at least one check-in place and a check-in place of the query user;计算查询用户与每个所述关联用户之间的相似度,并根据计算的所述相似度确定所述查询用户对应的相似用户集;Calculating a similarity between the query user and each of the associated users, and determining a similar user set corresponding to the query user according to the calculated similarity;确定所述相似用户集所对应的签到地点集合,所述签到地点集合中包括所述相似用户集中所有关联用户所签到的签到地点;及Determining a set of check-in locations corresponding to the similar set of users, where the set of check-in locations includes a check-in place signed by all associated users in the similar user set; and去除所述签到地点集合中与所述查询用户重合的签到地点,得到推荐地点集合,将所述推荐地点集合中所包含的推荐地点推送至所述查询用户终端。And removing the check-in place in the set of check-in locations that coincides with the query user, obtaining a set of recommended places, and pushing the recommended place included in the set of recommended places to the querying user terminal.
- 根据权利要求11所述的计算机设备,其特征在于,所述签到数据还包括对所述签到地点的评分;The computer device of claim 11, wherein the check-in data further comprises a rating of the check-in location;所述处理器执行的所述计算查询用户与每个所述关联用户之间的相似度,并根据计算的所述相似度确定所述查询用户对应的相似用户集,包括:The calculation performed by the processor queries the similarity between the user and each of the associated users, and determines the similar user set corresponding to the query user according to the calculated similarity, including:计算每个所述关联用户与所述查询用户之间的相似度,其中,所述查询用户与所述关联用户所签到的签到地点越集中、对共同签到地点的评分越接近,计算得到的相似度数值越大;Calculating a similarity between each of the associated users and the querying user, wherein the closer the check-in place that the querying user and the associated user sign in, the closer the score to the common sign-in place, and the calculated similarity The greater the degree value;将所述相似度大于设定阈值的所述关联用户组成所述查询用户的相似用户集。The associated users whose similarities are greater than a set threshold are grouped into similar user sets of the querying users.
- 根据权利要求12所述的计算机设备,其特征在于,所述处理器执行的所述计算每个所述关联用户与所述查询用户之间的相似度为:The computer device according to claim 12, wherein said calculating said similarity between each of said associated users and said querying user is:通过下述公式计算每个所述关联用户与所述查询用户之间的相似度:The similarity between each of the associated users and the querying user is calculated by the following formula:其中,u、v分别代表查询用户和关联用户;sim(u,v)为关联用户与查询用户之间的相似度;为关联用户与查询用户共同的签到地点;为关联用户与查询用户非共同的签到地点;Rui和Rvi分别为查询用户、关联用户对地点i的评分;rmax为位置社交网络平台中被任一用户签到次数最多的签到地点对应的签到次数。Where 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; A check-in location common to the associated user and the querying user; For the associated user and the query user, the check-in location is not common; 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.
- 根据权利要求12所述的计算机设备,其特征在于,所述处理器执行的所述去除所述签到地点集合中与所述查询用户重合的签到地点,得到推荐地点 集合,将所述推荐地点集合中所包含的推荐地点推送至所述查询用户终端,包括:The computer device according to claim 12, wherein the removing, by the processor, the check-in place in the set of check-in locations that coincides with the query user, to obtain a recommended place The collection, the recommended location included in the recommended location set is pushed to the query user terminal, including:去除所述签到地点集合中与所述查询用户重合的签到地点,得到待推荐地点集合;Removing a check-in place in the set of check-in locations that coincides with the query user, and obtaining a set of places to be recommended;计算所述查询用户与所述待推荐地点集合中每一个待推荐地点的兴趣度,将所述兴趣度大于设定阈值的所述待推荐地点推送至所述查询用户终端;Calculating the degree of interest of the to-be-recommended location in the set of the to-be-recommended locations, and pushing the to-be-recommended location with the interest degree greater than a set threshold to the querying user terminal;其中,所述兴趣度是通过所述查询用户与所述相似用户集中签到所述待推荐地点的关联用户之间的所述相似度以及所述关联用户对所述待推荐地点的评分计算得到的。The degree of interest is calculated by the similarity between the querying user and the related user of the similar user, and the rating of the to-be-recommended location by the associated user. .
- 根据权利要求14所述的计算机设备,其特征在于,所述查询用户与所述待推荐地点的兴趣度的计算公式为:The computer device according to claim 14, wherein the formula for calculating the degree of interest of the querying user and the location to be recommended is:其中,u为查询用户,j为确定的待推荐地点集合中的待推荐地点;U为所述查询用户的相似用户集,uk是所述相似用户集中的签到过所述待推荐地点j的关联用户;sim(u,uk)为查询用户u与关联用户uk之间的相似度,为关联用户uk对待推荐地点j的评分。Where u is the querying user, j is the determined to-be-recommended location in the set of to-be-recommended locations; U is a similar set of users of the querying user, and u k is the signing of the to-be-recommended location j of the similar user set Associated user; 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 .
- 一个或者多个存储有计算机可执行指令的非易失性可读存储介质,所述指令被一个或者多个处理器执行,使得所述一个或者多个处理器执行以下步骤: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:接收查询用户终端发送的地点推荐请求,所述地点推荐请求中携带查询用户标识;Receiving a location recommendation request sent by the user terminal, where the location recommendation request carries the query user identifier;查找与所述查询用户标识对应的查询用户的签到数据,其中,位置社交网络平台根据用户的历史签到行为生成签到数据集,所述签到数据集中每个用户的签到数据均包括签到地点;Searching for the check-in data of the query user corresponding to the query user identifier, where the location social network platform generates a 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 location;在所述签到数据集中查找所述查询用户的关联用户,所述关联用户至少有 一个签到地点与所述查询用户的签到地点重合;Searching, in the check-in data set, an associated user of the query user, where the associated user has at least A check-in location coincides with the check-in location of the querying user;计算查询用户与每个所述关联用户之间的相似度,并根据计算的所述相似度确定所述查询用户对应的相似用户集;Calculating a similarity between the query user and each of the associated users, and determining a similar user set corresponding to the query user according to the calculated similarity;确定所述相似用户集所对应的签到地点集合,所述签到地点集合中包括所述相似用户集中所有关联用户所签到的签到地点;及Determining a set of check-in locations corresponding to the similar set of users, where the set of check-in locations includes a check-in place signed by all associated users in the similar user set; and去除所述签到地点集合中与所述查询用户重合的签到地点,得到推荐地点集合,将所述推荐地点集合中所包含的推荐地点推送至所述查询用户终端。And removing the check-in place in the set of check-in locations that coincides with the query user, obtaining a set of recommended places, and pushing the recommended place included in the set of recommended places to the querying user terminal.
- 根据权利要求16所述的非易失性可读存储介质,其特征在于,所述签到数据还包括对所述签到地点的评分;The non-volatile readable storage medium of claim 16, wherein the check-in data further comprises a rating of the check-in location;所述处理器执行的所述计算查询用户与每个所述关联用户之间的相似度,并根据计算的所述相似度确定所述查询用户对应的相似用户集,包括:The calculation performed by the processor queries the similarity between the user and each of the associated users, and determines the similar user set corresponding to the query user according to the calculated similarity, including:计算每个所述关联用户与所述查询用户之间的相似度,其中,所述查询用户与所述关联用户所签到的签到地点越集中、对共同签到地点的评分越接近,计算得到的相似度数值越大;Calculating a similarity between each of the associated users and the querying user, wherein the closer the check-in place that the querying user and the associated user sign in, the closer the score to the common sign-in place, and the calculated similarity The greater the degree value;将所述相似度大于设定阈值的所述关联用户组成所述查询用户的相似用户集。The associated users whose similarities are greater than a set threshold are grouped into similar user sets of the querying users.
- 根据权利要求17所述的非易失性可读存储介质,其特征在于,所述处理器执行的所述计算每个所述关联用户与所述查询用户之间的相似度为:The non-volatile readable storage medium according to claim 17, wherein said calculating said similarity between each of said associated users and said querying user is:通过下述公式计算每个所述关联用户与所述查询用户之间的相似度:The similarity between each of the associated users and the querying user is calculated by the following formula:其中,u、v分别代表查询用户和关联用户;sim(u,v)为关联用户与查询用户之间的相似度;为关联用户与查询用户共同的签到地点;为关联用户与查询用户非共同的签到地点;Rui和Rvi分别为查询用户、关联用户对地点i的评分;rmax为位置社交网络平台中被任一用户签到次数最多的签到地点对应的签到次数。Where 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; A check-in location common to the associated user and the querying user; For the associated user and the query user, the check-in location is not common; 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.
- 根据权利要求17所述的非易失性可读存储介质,其特征在于,所述处 理器执行的所述去除所述签到地点集合中与所述查询用户重合的签到地点,得到推荐地点集合,将所述推荐地点集合中所包含的推荐地点推送至所述查询用户终端,包括:A non-volatile readable storage medium according to claim 17, wherein said The method of performing the process of removing the check-in place in the set of check-in locations that coincides with the query user, obtaining a set of recommended places, and pushing the recommended place included in the set of recommended places to the querying user terminal, including:去除所述签到地点集合中与所述查询用户重合的签到地点,得到待推荐地点集合;Removing a check-in place in the set of check-in locations that coincides with the query user, and obtaining a set of places to be recommended;计算所述查询用户与所述待推荐地点集合中每一个待推荐地点的兴趣度,将所述兴趣度大于设定阈值的所述待推荐地点推送至所述查询用户终端;Calculating the degree of interest of the to-be-recommended location in the set of the to-be-recommended locations, and pushing the to-be-recommended location with the interest degree greater than a set threshold to the querying user terminal;其中,所述兴趣度是通过所述查询用户与所述相似用户集中签到所述待推荐地点的关联用户之间的所述相似度以及所述关联用户对所述待推荐地点的评分计算得到的。The degree of interest is calculated by the similarity between the querying user and the related user of the similar user, and the rating of the to-be-recommended location by the associated user. .
- 根据权利要求19所述的非易失性可读存储介质,其特征在于,所述查询用户与所述待推荐地点的兴趣度的计算公式为:The non-volatile readable storage medium according to claim 19, wherein the formula for calculating the degree of interest of the querying user and the location to be recommended is:其中,u为查询用户,j为确定的待推荐地点集合中的待推荐地点;U为所述查询用户的相似用户集,uk是所述相似用户集中的签到过所述待推荐地点j的关联用户;sim(u,uk)为查询用户u与关联用户uk之间的相似度,为关联用户uk对待推荐地点j的评分。 Where u is the querying user, j is the determined to-be-recommended location in the set of to-be-recommended locations; U is a similar set of users of the querying user, and u k is the signing of the to-be-recommended location j of the similar user set Associated user; 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 .
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