Time-aware location recommendation method based on fuzzy clustering for location social network

Abstract

The present invention discloses a time-aware position recommendation method based on fuzzy clustering for positional social network, including: step 1, obtaining user sign-in data information, including user information, position information and time information; step 2, through the The location information extracts the geographical feature of the access location, extracts the user time feature through the time information, obtains the user feature vector according to the geographical feature of the access location and the user time feature, and obtains a position recommendation based on the user fuzzy clustering algorithm; After the time information and the position information calculate the position attractiveness of each position in each time period, a position recommendation based on the position attractiveness is obtained; step 3, according to the position recommendation, the collaborative filtering method is used to predict the user's perception of each position under time perception. unchecked-in access value; step 4, given the target user and time, filter the position of each unchecked-in access value Top-N, and recommend to the user.

Description

Technical field [0001] The present invention relates to the location recommendation in the social network, and in particular, to a positional social network-based, ambiguated positionally recommended location recommended method. Background technique [0002] In the context of mass information, the recommended system can implement information screening according to user preferences, effectively solve information multiple load problems, the recommended system can solve the problem of information overload, which has caused extensive attention in the industry, and the location is based on location-based social network ( An application in LBSN: Location based Social Network is designed to recommend a location that may be interested in interested, is an important means to realize user personalization needs and resolve information filtering. [0003] LBSN is a new social network of location services and traditional social networks. It records the location of the user in the real world. ...

AI Technical Summary

Problems solved by technology

[0006] (1) The current recommendation algorithm in the location-based social network mainly realizes recommending a suitable location to a single user. However, it does not comprehensively consider the influence of contextual information such as time and geographic location on location recommendation, which restricts the timeliness and accuracy of recommendation to a certain extent. sex;

[0007] (2) Based on the user collaborative filtering method, without considering the group characteristics of users, recommend users in similar groups, but recommend target users based on the similarity of all users, which will make those irrelevant users also Add it to the recommendation evaluation, and further increase the complexity of calculation;

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