POI recommendation method combining travel interest and social preference

Abstract

The invention discloses a POI recommendation method combining travel interest and social preference, and the method comprises the steps: learning a user travel behavior according to the historical POIdata distribution of a user in an LBSN, and predicting a POI accessed by the user in the future travel according to the current position; Constructing social contact associated interest similarity byextracting theme vectors; Constructing a heterogeneous travel information network, and establishing interest similarity of travel behaviors; Determining a similar group by integrating social interestsimilarity analysis and travel behavior similarity analysis; Generating a candidate POI set by combining the predicted POI of the future travel access of the user and the similar groups of the user,and discovering TOP-N POI that users are most likely to go to by the calculation. According to the method, the similar groups of the user are discovered by utilizing social interest and travel preferences while position prediction is considered, more proper interest point recommendation can be comprehensively provided for the user by utilizing the similar groups instead of friend users, and the problem of data sparsity in the LBSN is relieved, so that the recommendation effect can be better improved.

Description

technical field

[0001] The invention belongs to the field of behavior recognition, and in particular relates to a POI recommendation method combining travel interests and social preferences. Background technique

[0002] With the continuous development of mobile social networks, using the location information released by users to provide them with region-based personalized recommendation services not only provides convenience to users, but also brings huge potential benefits to merchants. As a key technology in such services, location prediction technology is one of the important research contents in location-based social networks. In social networks based on location prediction, users often share some relevant location information about themselves, such as comments, pictures, and text. Personalized POI recommendation is an important task of LBSN (location-based social network), such as recommending movie theaters, restaurants, tourist attractions, etc. Promote users to be...

Examples

Embodiment Construction

[0067] The POI recommendation method combining travel interests and social preferences proposed by the present invention will be described in detail below.

[0068] see figure 1 , the present invention comprises the following steps:

[0069] Step 1: First, learn user travel behavior according to the distribution of user historical Point of Interest (POI) data in the LBSN (Location-Based Social Network), and use the variable-order Markov algorithm to predict the POI that the user will visit in the future based on the current location. Reach the semantic information of the POI; the specific process is as follows:

[0070] (1) Firstly, a point-of-interest sequence is established by using the points-of-interest of the user's daily travel. The point of interest (POI) sequence is defined as: Seq_Loc=loc 0 →…→loc i →…→loc h ,

[0071] where loc i Represents a certain location point, loc i =(lat,lon,check_in time ,POI i ,POI_category j ). Where lat, lon represent the longi...

technical field

[0001] The invention belongs to the field of behavior recognition, and in particular relates to a POI recommendation method combining travel interests and social preferences. Background technique

[0002] With the continuous development of mobile social networks, using the location information released by users to provide them with region-based personalized recommendation services not only provides convenience to users, but also brings huge potential benefits to merchants. As a key technology in such services, location prediction technology is one of the important research contents in location-based social networks. In social networks based on location prediction, users often share some relevant location information about themselves, such as comments, pictures, and text. Personalized POI recommendation is an important task of LBSN (location-based social network), such as recommending movie theaters, restaurants, tourist attractions, etc. Promote users to be...

Examples

Embodiment Construction

[0067] The POI recommendation method combining travel interests and social preferences proposed by the present invention will be described in detail below.

[0068] see figure 1 , the present invention comprises the following steps:

[0069] Step 1: First, learn user travel behavior according to the distribution of user historical Point of Interest (POI) data in the LBSN (Location-Based Social Network), and use the variable-order Markov algorithm to predict the POI that the user will visit in the future based on the current location. Reach the semantic information of the POI; the specific process is as follows:

[0070] (1) Firstly, a point-of-interest sequence is established by using the points-of-interest of the user's daily travel. The point of interest (POI) sequence is defined as: Seq_Loc=loc 0 →…→loc i →…→loc h ,

[0071] where loc i Represents a certain location point, loc i =(lat,lon,check_in time ,POI i ,POI_category j ). Where lat, lon represent the longi...

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