TW201933879A - Method and device for content recommendation - Google Patents

Abstract

Provided by an embodiment disclosed in the description is a content recommendation method, the method comprising: obtaining location data of a user, and determining location information corresponding to the location data according to a location mapping model; then, at least according to the determined location information and feature information of various content information in a content information library, determining content recommendation information recommended to the user.

Description

Content recommendation method and device

The embodiments disclosed in the present disclosure relate to the field of Internet technologies, and in particular, to a content recommendation method and apparatus.

With the development of Internet technology, people are more and more frequently browsing the content information provided by the network platform. For example, browse product information in the online shopping platform, or browse hot information on the news platform, or browse financial information on the financial platform.
Different users have more or less different requirements for the content information they provide when using the same network platform. In another aspect, the huge amount of information in the web platform often makes it difficult for users to choose. At present, content information recommended to users is insufficiently accurate, not timely enough, etc., and it is difficult to meet the requirements of users. Therefore, there is a need to provide a reasonable method to meet the various needs of users to browse the content information provided in the network platform.

The present specification describes a content recommendation method and apparatus for determining content recommendation information recommended to a user according to the location information of the user and the feature information of each content information in the content information database, thereby promptly recommending more accurate content information to the user.
In the first aspect, a content recommendation method is provided. The method includes:
Obtain the user's location data;
Determining, according to the location mapping model, location information corresponding to the location data, where the location mapping model is obtained based on pre-acquired service information, where the service information includes service location data and corresponding service location data;
Determining the content recommendation information recommended to the user according to at least the location information and the feature information of each content information in the content information database.
In a possible implementation manner, the location mapping model is obtained through the following steps:
Clustering the service location data included in the service information to obtain a plurality of clusters;
Determining the location label corresponding to the plurality of clusters according to the service location data corresponding to the service location data in the service information.
In a possible implementation manner, the determining location information corresponding to the location data includes:
Determining a cluster corresponding to the location data of the user from the plurality of clusters;
The location information is determined according to the location label corresponding to the determined cluster.
In a possible implementation manner, the determining content recommendation information recommended to the user includes:
Determining service category information corresponding to the location information;
Determining, according to the feature information of the respective content information, content information related to the service category information from the content information database, and using the content information as the content recommendation information.
In a possible implementation, the feature information includes the service category information of the content information, and the determining content information related to the service category information from the content information database includes:
Determining the content information corresponding to the service category information from the content information library.
In a possible implementation manner, the feature information of each content information includes a predetermined location information corresponding to the content information, and the determining content recommendation information recommended to the user includes:
When the affiliation relationship between the location information corresponding to the content information and the location information corresponding to the location data conforms to a preset rule, the content information is used as the content recommendation information.
In a possible implementation manner, the feature information is determined based on keyword information and location information extracted from the content information.
In a possible implementation manner, the location data includes at least one of a wireless fidelity WiFi fingerprint data and latitude and longitude data.
In a possible implementation manner, the location information includes at least one of a building name, a business name, and a business circle information.
In a second aspect, a content recommendation device is provided. The device includes:
An obtaining unit, configured to acquire location information of the user;
a determining unit, configured to determine, according to the location mapping model, location information corresponding to the location data, where the location mapping model is trained based on pre-acquired service information, where the service information includes service location data and a corresponding service location data;
The processing unit is configured to determine content recommendation information recommended to the user according to at least the location information and the feature information of each content information in the content information database.
In a possible implementation manner, the location mapping model is obtained by the determining unit by the following steps:
Clustering the service location data included in the service information to obtain a plurality of clusters;
Determining the location label corresponding to the plurality of clusters according to the service location data corresponding to the service location data in the service information.
In a possible implementation, the determining unit specifically includes:
a first determining subunit, configured to determine, from the plurality of clusters, a cluster corresponding to the location data of the user;
And a second determining subunit, configured to determine the location information according to the location label corresponding to the determined cluster.
In a possible implementation, the processing unit specifically includes:
a first processing subunit, configured to determine service category information corresponding to the location information;
The second processing sub-unit is configured to determine, according to the feature information of the respective content information, content information related to the service category information from the content information database, and use the content information as the content recommendation information.
In a possible implementation, the feature information in the second processing sub-unit includes the service category information of the content information, and the second processing sub-unit is specifically configured to:
Determining the content information corresponding to the service category information from the content information library.
In a possible implementation, the feature information of each content information included in the processing unit includes a predetermined location information corresponding to the content information, and the processing unit is specifically configured to:
When the affiliation relationship between the location information corresponding to the content information and the location information corresponding to the location data conforms to a preset rule, the content information is used as the content recommendation information.
In a possible implementation manner, the feature information included in the processing unit is determined based on keyword information and location information extracted from the content information.
In a possible implementation manner, the location information acquired by the acquiring unit includes at least one of a wireless fidelity WiFi fingerprint data and latitude and longitude data.
In a possible implementation manner, the location information determined by the determining unit includes at least one of a building name, a merchant name, and a business circle information.
In a third aspect, a computer readable storage medium is provided having a computer program stored thereon. When the computer program is executed in a computer, the computer is caused to perform the method provided by any of the first aspects described above.
In a fourth aspect, a computing device is provided that includes a storage and a processor. An executable code is stored in the storage, and when the processor executes the executable code, the method provided by any one of the foregoing first aspects is implemented.
The content recommendation method and device provided by the present specification determine the location information corresponding to the location data by acquiring the location data of the user and according to the location mapping model. Then, at least according to the location information and the feature information of each content information in the content information database, the content recommendation information recommended to the user is determined, so that more accurate content information is recommended to the user in time.

The various embodiments disclosed herein are described below in conjunction with the drawings.
FIG. 1 is a schematic diagram of an application scenario of a content recommendation method according to an embodiment of the present disclosure. The execution body of the recommendation method may be a server. When a user logs in to a content recommendation platform (for example, an Alipay application platform) through a terminal (for example, the terminal can be a mobile phone, a tablet, a wearable smart device, etc.), the recommendation of the service function provided by the multiple embodiments disclosed in the present specification may be adopted. Method, the server obtains the location data of the user (eg, latitude and longitude data), and according to the location mapping model (eg, the location mapping model may include a mapping relationship between the location data and the location information), determining location information corresponding to the location data (eg, , car 4S shop). Then, based on the location information and the feature information of each content information in the content information database (eg, the feature information may include service category information), content recommendation information (eg, car maintenance knowledge, etc.) recommended to the user is determined.
The recommendation method of the business function provided by the multiple embodiments disclosed in the present specification determines the location information corresponding to the location data by acquiring the location data of the user and according to the location mapping model. Then, at least according to the location information and the feature information of each content information in the content information database, the content recommendation information recommended to the user is determined, so that more accurate content information is recommended to the user in time.
FIG. 2 is a flowchart of a content recommendation method according to an embodiment of the present disclosure. The execution subject of the method may be a device having processing capabilities: a server or a system or device, such as the server shown in FIG. As shown in FIG. 2, the method specifically includes:
Step S210: Acquire location information of the user.
Specifically, the location data may be data collected from a user's terminal by a Location Based Service (LBS). The LBS includes multiple positioning methods, such as Global Positioning System (GPS) positioning, base station positioning, and Wireless Fidelity (WiFi) positioning.
In one embodiment, the location data may include latitude and longitude data obtained by GPS location or base station location. For example, the latitude and longitude data included in the location data are: north latitude 39°54'25.70" and east longitude 116°23'28.49".
In another embodiment, the location profile may include WiFi fingerprint data obtained by WiFi location. In one example, the WiFi fingerprint data may include an address of a WiFi access point (AP) (eg, a Media Access Control (MAC) address) and a corresponding signal strength.
Step S220, determining location information corresponding to the location data of the user according to the location mapping model.
Specifically, the difference between the location information and the location data is that the location information is location information that the user can perceive and has clear semantics. In one embodiment, the venue information may include a building name (eg, New Zhongguan Building), a business name (eg, a Hamm toy store), and a business district information (eg, Sanlitun Taikooli). In another embodiment, the venue information may also include service category information (eg, food, clothing, beauty, movies, etc.) associated with the venue (eg, merchant).
The conversion from the location data to the location information can be performed according to a pre-obtained location mapping model. In one embodiment, the location mapping model includes a mapping relationship between the GPS coordinate data and the location tag, and the mapping relationship is obtained in advance by manual acquisition.
In another embodiment, the location mapping model may be trained and obtained based on the service location data and the corresponding service location data included in the pre-acquired service information. The service corresponding to the service information may include a payment service (eg, payment using an payment application) and a location service (eg, locating the current location when the social platform sends status information).
Among them, service information can be obtained in a variety of ways. In one embodiment, the service corresponding to the service information may include a payment service, and the corresponding service information may include payment information. Obtaining the payment information may include: the server may collect the payment information when detecting that the user submits the order through the terminal or pays the order, specifically including collecting the location information of the terminal as the service location information in the payment information, and submitting the The service location information corresponding to the service location data is obtained in the order. In an example, when a user performs a payment operation using a payment application (eg, Alipay) on a terminal connected to WiFi, the server of the payment application can collect the location data of the terminal at this time through WiFi positioning (eg, WiFi fingerprint data). ) and information on the service areas included in the payment information (eg, McDonald's College Road Shop).
In another embodiment, the service corresponding to the service information may include a location service, and the corresponding service information may include location service information. Obtaining the location service information may include: when the server detects that the user uses the location service by using the terminal, the server may obtain the location service information, specifically, the location information of the collection terminal is used as the service location data in the location service information, and the user is used in the location information. Location information selected or created at the time of service. In one example, the user uses a terminal that supports GPS positioning to log in to a work application (eg, a nail) to perform a work punching operation. At this time, the location information (eg, latitude and longitude data) of the collection terminal and the punch information may be collected by GPS. Service location information (eg, California gourmet restaurant). In another example, the user uses the terminal connected to the cellular network to publish the status including the location information on the social platform. At this time, the location information (eg, latitude and longitude data) of the collection terminal can be collected by the base station and the user selects (or creates ) Service location information (eg, one or two cafes).
Further, based on the pre-acquired service information, the location mapping model can be obtained through the following steps: clustering the service location data included in the service information to obtain multiple clusters. Then, the location label corresponding to the plurality of clusters is determined according to the service location data corresponding to the service location data in the service information. In this way, a mapping relationship between multiple clusters and multiple place tags can be established.
In one embodiment, the clustering algorithm may be used to cluster the service location data and obtain multiple clusters. The clustering algorithm may be a GEOSHASH algorithm or a DBSCAN algorithm, which is not limited herein.
In one example, the service location data includes a plurality of latitude and longitude data, and the GEOSHASH algorithm can be used to convert the data into a GEOHASH grid, and the grid number of each latitude and longitude data is determined. For example, the service location data includes 100 latitude and longitude coordinates, and the 100 latitude and longitude coordinates are converted into a GEOHASH grid by the GEOHASH algorithm. As shown in FIG. 3, each 20 latitude and longitude coordinates have the same mesh number, and the obtained 5 meshes are obtained. The grid numbers are: WX4G01, WX4H02, WX4I03, WX4J04, WX4K05. Thus, the above 100 latitude and longitude coordinates are clustered into 5 clusters.
In another example, the service location data includes a plurality of WiFi fingerprint data. In this case, the data may be clustered by using a DBSCAN algorithm to obtain a plurality of clusters having different numbers. The DBSCAN algorithm is a density-based clustering algorithm. Unlike the partitioning and hierarchical clustering methods, it defines the cluster as the largest set of points connected by density, can divide the area with sufficient high density into clusters, and can find clusters of arbitrary shapes in the spatial database of noise. . Specifically, in the DBSCAN algorithm, all the location points are first marked as core points, boundary points or noise points, and the noise points are deleted. Then assign an edge between all the core points within the preset parameters, each group of connected core points form a cluster, and assign each boundary point to a cluster of core points associated with it, thereby completing Clustering of location points.
It can be understood that the precision of the cluster obtained by the clustering (for example, the range size of the cluster) can be controlled by adjusting the parameters of the clustering algorithm. For example, the accuracy of the cluster can be controlled by controlling the number of coded bits in the GEOSHASH algorithm that encodes the service location data. More specifically, the more coded bits of the service location data, the more accurate the resulting cluster range. For another example, the accuracy of the cluster can be controlled by controlling the size of the neighborhood radius ε in the DBSCAN algorithm. More specifically, the smaller the value of the input domain radius ε, the more accurate the resulting cluster size range.
In an embodiment, for the plurality of clusters obtained by the cluster, the location labels corresponding to the clusters in the plurality of clusters may be determined according to the service location data corresponding to the service location data in the service information.
In an example, if the service location data corresponding to the service location data includes the name of the service location (for example, a happiness supermarket), the name of the service location may be used as a location label corresponding to the cluster of the service location data.
In another example, a plurality of service location data may be corresponding to a single cluster, and a range (eg, a merchant or a business circle) identified by multiple service location materials corresponding to multiple service location data may be different. Accordingly, a single cluster can have multiple place tags, such as a merchant tag and a business circle tag. For example, a place label for a cluster may include a merchant label (eg, West MCA) and a business circle label (eg, Dazhong Temple).
In this step, determining the location information corresponding to the location data of the user may include: determining a cluster corresponding to the location data of the user from the plurality of clusters obtained by the cluster. Then, the location information is determined according to the location tag corresponding to the determined cluster.
In one embodiment, the location mapping model includes location data of a center point of each cluster cluster among the plurality of clusters, and a mapping relationship between the plurality of cluster clusters and the plurality of location labels. In this manner, determining a cluster corresponding to the location data of the user from the plurality of clusters obtained by the clustering may include: calculating a distance between the location data of the user and the location data of each central point, and calculating a minimum value of the distance The corresponding cluster is used as a cluster corresponding to the location data of the user.
Further, determining the location information according to the location label corresponding to the determined cluster may include: mapping the relationship between the plurality of clusters and the plurality of location labels, and determining the cluster corresponding to the location data of the user. Determine the location label corresponding to the cluster to determine the location information.
In one embodiment, the determined location tag includes a merchant tag (eg, West MCA), and the location information including the merchant name (eg, West MCA) may be determined accordingly.
After determining the location information corresponding to the location data of the user in step S220, next, in step S230, the content recommendation information recommended to the user is determined based on at least the location information and the feature information of each content information in the content information repository.
Specifically, the determining the content recommendation information recommended by the user may include: determining service category information corresponding to the location information, and determining content information related to the service category information from the content information database according to the feature information of each content information, and Use this content information as content recommendation information.
In an embodiment, determining the content information related to the service category information from the content information base may include: calculating, according to the feature information of each content information, the relevance of each content information and the service category information, and calculating the correlation according to the Determine the content information related to the service category information.
In one example, content information having a relevance within a predetermined range (eg, a predetermined range may be greater than 0.6) may be determined as content information related to the service category information. In another example, the content information may be ranked according to the relevance, and the content information within the predetermined range (eg, the predetermined range may be the top five) may be determined as the content information related to the service category information.
In another embodiment, the feature information may include service category information of the content information. Determining the content information related to the service category information from the content information base may include: determining content information corresponding to the service category information from the content information base.
In one example, the location information determined in step S220 is “automobile 4S shop”, and accordingly, the service category information corresponding to the location information may be determined to be “car”. Correspondingly, the content information of the service category information is “car” can be determined from the content information base, and the content information is used as the content recommendation information recommended to the user.
Alternatively, the feature information of each content information may include predetermined location information corresponding to the content information. In this case, determining the content recommendation information recommended to the user may include: when the affiliation relationship between the location information corresponding to each content information and the location information corresponding to the location data conforms to the preset rule, the content information is As content recommendation information.
In one embodiment, the preset rules may be determined by the business party based on the attributes of the business (eg, the accuracy requirements of the business for the location of the venue). For example, when the service is to push the merchant coupon to the user, the preset rule may include the location information corresponding to the content recommendation information and the location information corresponding to the location data. When the service is to recommend similar places in the vicinity to the user, the preset rule may include that the location information corresponding to the content recommendation information is the same as the location information corresponding to the location data.
In an example, the preset rule includes that the location information corresponding to the content recommendation information and the location information corresponding to the location data are completely the same, and the location information corresponding to the location data includes “the old delicious pot” and “Sanlitun”. Correspondingly, content information (such as merchant discounts, merchant public number information, and merchant evaluation information) corresponding to the location information "old and delicious" (business) and "Sanlitun" (commercial circle) can be used as content. Recommended information.
In another example, the preset rule may include that the location information corresponding to the content recommendation information is the same as the location information portion corresponding to the location data, and the location information corresponding to the location data includes “Uniqlo” (business), “Sanlitun” (Business circle). Correspondingly, content information corresponding to the location information "Sanlitun" (for example, clothing stores, restaurants, etc. in the Sanlitun business district) can be used as content recommendation information.
After the content recommendation information recommended by the user is determined through the foregoing steps, the method further includes: sending the content recommendation information to the user. For example, when the user opens the corresponding application App, or opens the recommended channel, or when it detects that the location of the user changes, the latest location information of the user is obtained, and the content recommendation information recommended to the user is determined according to the content recommendation information, and the user is sent to the user. Content recommendation information.
It should be noted that, in step S220, the method further includes: determining service category information corresponding to the location information. In one embodiment, the location information may include service category information corresponding to the location in addition to the name of the location, and accordingly, the service category information corresponding to the location information may be directly determined according to the location information.
In another embodiment, after the location information is determined, the service category information corresponding to the location information may be determined according to the correspondence between the pre-stored plurality of location information and the plurality of service category information. In one example, the identified location tag includes a merchant tag (eg, West MCA), which can be used to determine a business name (eg, West MCA) and service category information (eg, food) corresponding to the merchant name.
The feature information mentioned in step S230 may be determined based on keyword information and/or location information extracted from the content information, and may be determined by using the method shown in FIG. 4:
Step S410, preprocessing the content information.
Specifically, the pre-processing may include structured analysis, word segmentation processing, de-stop word processing, word-of-speech (postag), and the like. Wherein, the structured analysis may include an analysis of the paragraph structure in the content information, for example, determining the title and the body in the content information; the word segmentation processing may include a unigram, a bigram, and a trigram. Waiting for the stop word can include removing the stop words in the content information according to the preset stop word list (eg, non-meaningful function words: this, that,); part of speech tagging refers to content information The part of speech (such as nouns, adverbs, adjectives, etc.) is marked.
Step S420, extracting keyword information according to the pre-processed content information.
Specifically, the position of the word obtained by the preprocessing in the content information (for example, in the title or in the text), the part of the tag, the TextRank algorithm or the TF-IDF (Term Frequency–inverse Document Frequency) algorithm may be used. Weighted to identify keyword information.
Step S430, extracting location information according to the pre-processed content information.
Specifically, the location information may be extracted according to the location tag included in the content information, and the location information may include the location information. In one embodiment, the location tag may be a location tag for the content information producer when posting the content information, such as Beijing, Hangzhou, and the like.
Alternatively, a method of Named Entity Recognition (NER) may be used to identify place names, institution names, and the like in the content information. For example, it can be identified that the name of the content information is Wutai Mountain, and the institution is called Haidian District Civil Affairs Bureau (belonging to the location information).
Alternatively, the server may pre-store a location information database, and accordingly, the location information matching the information in the location information database may be extracted from the pre-processed content information.
Step S440, input the initially extracted keyword information and location information into the pre-trained feature extraction model to determine the feature information.
Specifically, the feature extraction model may be an offline training of large-scale content corpus data, and a word embedding model or a NER model based on Recurrent Neural Networks (RNN).
Further, the determination of the above feature information may be performed within a predetermined time (for example, 5 min or 10 min) after the content information is generated.
It can be seen from the above that in the content recommendation method provided by the multiple embodiments disclosed in the present specification, the location information corresponding to the location data is determined by acquiring the location data of the user and according to the location mapping model. Then, at least according to the location information and the feature information of each content information in the content information database, the content recommendation information recommended to the user is determined, so that more accurate content information is recommended to the user in time.
Corresponding to the content recommendation method, the multiple embodiments disclosed in the present specification further provide a content recommendation device. As shown in FIG. 5, the device 500 includes:
The obtaining unit 510 is configured to acquire location information of the user.
The determining unit 520 is configured to determine, according to the location mapping model, the location information corresponding to the location data, where the location mapping model is trained based on the pre-acquired service information, where the service information includes the service location data and the corresponding service location data;
The processing unit 530 is configured to determine content recommendation information recommended to the user according to at least the location information and the feature information of each content information in the content information database.
In a possible implementation, the location mapping model is obtained by the determining unit 520 through the following steps:
Clustering the service location data contained in the service information to obtain multiple clusters;
The location label corresponding to the plurality of clusters is determined according to the service location data corresponding to the service location data in the service information.
In a possible implementation, the determining unit 520 specifically includes:
a first determining subunit 521, configured to determine a cluster corresponding to the location data of the user from the plurality of clusters;
The second determining subunit 522 is configured to determine the location information according to the location label corresponding to the determined cluster.
In a possible implementation, the processing unit 530 specifically includes:
The first processing sub-unit 531 is configured to determine service category information corresponding to the location information;
The second processing sub-unit 532 is configured to determine content information related to the service category information from the content information base according to the feature information of each content information, and use the content information as the content recommendation information.
In a possible implementation manner, the feature information in the second processing sub-unit 532 includes service category information of the content information, and the second processing sub-unit 532 is specifically configured to:
The content information corresponding to the service category information is determined from the content information library.
In a possible implementation, the feature information of each content information included in the processing unit 530 includes a predetermined location information corresponding to the content information, and the processing unit 530 is specifically configured to:
When the affiliation between the location information corresponding to each content information and the location information corresponding to the location data conforms to the preset rule, the content information is used as the content recommendation information.
In a possible implementation manner, the feature information included in the processing unit 530 is determined based on the keyword information and the location information extracted from the content information.
In a possible implementation, the location information acquired by the obtaining unit 510 includes at least one of a wireless fidelity WiFi fingerprint data and latitude and longitude data.
In a possible implementation manner, the location information determined by the determining unit 520 includes at least one of a building name, a merchant name, and a business circle information.
It can be seen from the above that in the content recommendation apparatus provided by the multiple embodiments disclosed in the present specification, the obtaining unit 510 acquires the location data of the user, and the determining unit 520 determines the location information corresponding to the location data according to the location mapping model. The processing unit 530 determines the content recommendation information recommended to the user according to the location information and the feature information of each content information in the content information database, so as to promptly recommend more accurate content information to the user.
Those of ordinary skill in the art should recognize that the functions described in the various embodiments disclosed herein may be in the form of hardware, software, firmware, or any combination thereof. achieve. When implemented in software, these functions may be stored in a computer readable medium or transmitted as one or more instructions or code on a computer readable medium.
The embodiments, the technical solutions, and the beneficial effects of the various embodiments disclosed in the present specification are further described in detail. It should be understood that the foregoing is only the implementation of the embodiments disclosed in the specification. The manner of the present invention is not limited to the scope of protection of the various embodiments disclosed herein, and any modifications, improvements, improvements, etc., which are based on the technical solutions of the various embodiments disclosed herein, should be It is intended to be included within the scope of the various embodiments disclosed herein.

500‧‧‧ device

510‧‧‧Acquisition unit

520‧‧‧Determining unit

521‧‧‧First determined subunit

522‧‧‧Secondary subunit

530‧‧‧Processing unit

531‧‧‧First Processing Subunit

532‧‧‧Second processing subunit

S210‧‧‧Steps

S220‧‧‧Steps

S230‧‧‧Steps

S410‧‧‧Steps

S420‧‧‧ steps

S430‧‧‧Steps

S440‧‧‧Steps

In order to more clearly illustrate the technical solutions of the various embodiments disclosed in the specification, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only disclosed in the present specification. A plurality of embodiments, for those of ordinary skill in the art to which the invention pertains, may obtain other drawings based on these drawings without undue creative effort.

FIG. 1 is a schematic diagram of an application scenario of a content recommendation method according to an embodiment of the present disclosure;

FIG. 2 is a flowchart of a content recommendation method according to an embodiment of the present disclosure;

FIG. 3 is a schematic diagram of clustering service location data into multiple clusters according to an embodiment of the present disclosure;

FIG. 4 is a flowchart of a feature information determining method according to an embodiment of the present disclosure;

FIG. 5 is a schematic diagram of a content recommendation apparatus according to an embodiment of the present disclosure.

Claims (18)

A content recommendation method, comprising: Obtain the user's location data; Determining, according to the location mapping model, location information corresponding to the location data, where the location mapping model is obtained based on pre-acquired service information, where the service information includes service location data and corresponding service location data; Determining the content recommendation information recommended to the user according to at least the location information and the feature information of each content information in the content information database. The method of claim 1, wherein the location mapping model is obtained by training in the following steps: Clustering the service location data included in the service information to obtain a plurality of clusters; Determining the location label corresponding to the plurality of clusters according to the service location data corresponding to the service location data in the service information. The method of claim 2, wherein the determining the location information corresponding to the location data comprises: Determining a cluster corresponding to the location data of the user from the plurality of clusters; The location information is determined according to the location label corresponding to the determined cluster. The method of claim 1, wherein the determining content recommendation information recommended to the user comprises: Determining service category information corresponding to the location information; Determining, according to the feature information of the respective content information, content information related to the service category information from the content information database, and using the content information as the content recommendation information. The method of claim 4, wherein the feature information includes the service category information of the content information, and the determining content information related to the service category information from the content information database comprises: Determining the content information corresponding to the service category information from the content information library. The method of claim 1, wherein the feature information of each content information comprises predetermined location information corresponding to the content information, and the determining content recommendation information recommended to the user comprises: When the affiliation relationship between the location information corresponding to the content information and the location information corresponding to the location data conforms to a preset rule, the content information is used as the content recommendation information. The method of any one of claims 1 to 6, wherein the feature information is determined based on keyword information and location information extracted from the content information. The method of any one of claims 1 to 6, wherein the location profile comprises at least one of wireless fidelity WiFi fingerprint data and latitude and longitude data. The method of any one of claims 1 to 6, wherein the location information comprises at least one of a building name, a business name, and a business circle information. A content recommendation device, comprising: An obtaining unit, configured to acquire location information of the user; a determining unit, configured to determine, according to the location mapping model, location information corresponding to the location data, where the location mapping model is trained based on pre-acquired service information, where the service information includes service location data and a corresponding service location data; The processing unit is configured to determine content recommendation information recommended to the user according to at least the location information and the feature information of each content information in the content information database. The apparatus of claim 10, wherein the location mapping model is obtained by the determining unit by the following steps: Clustering the service location data included in the service information to obtain a plurality of clusters; Determining the location label corresponding to the plurality of clusters according to the service location data corresponding to the service location data in the service information. The device of claim 11, wherein the determining unit specifically includes: a first determining subunit, configured to determine, from the plurality of clusters, a cluster corresponding to the location data of the user; And a second determining subunit, configured to determine the location information according to the location label corresponding to the determined cluster. The device of claim 10, wherein the processing unit specifically includes: a first processing subunit, configured to determine service category information corresponding to the location information; The second processing sub-unit is configured to determine, according to the feature information of the respective content information, content information related to the service category information from the content information database, and use the content information as the content recommendation information. The device of claim 13, wherein the feature information in the second processing sub-unit includes the service category information of the content information, and the second processing sub-unit is specifically configured to: Determining the content information corresponding to the service category information from the content information library. The device of claim 10, wherein the feature information of each content information included in the processing unit includes a predetermined location information corresponding to the content information, and the processing unit is specifically configured to: When the affiliation relationship between the location information corresponding to the content information and the location information corresponding to the location data conforms to a preset rule, the content information is used as the content recommendation information. The apparatus according to any one of claims 10 to 15, wherein the feature information included in the processing unit is determined based on keyword information and location information extracted from the content information. The device of any one of claims 10 to 15, wherein the location data acquired by the acquisition unit comprises at least one of wireless fidelity WiFi fingerprint data and latitude and longitude data. The apparatus of any one of claims 10 to 15, wherein the location information determined by the determining unit comprises at least one of a building name, a merchant name, and a business circle information.