A Tag-Based Integrated Diffusion Model for Personalized Location Recommendation

Abstract

The location based services have attracted millions of users to share their locations via check-ins. It is highly important to recommend personalized POIs (Points-Of-Interest) to users in terms of their preference learned from historical data. In current research work, users’ check-in behavior is wildly used to model user’s preference. However, the sparsity of the check-in data makes it difficult to capture users’ preferences accurately. This paper proposes a tag-based integrated diffusion recommender system for location recommendation, considering not only social influence but also venue features. Firstly, we model user location preference by combining the preference extracted from check-ins data and short text tips, where sentiment analysis techniques are used. Furthermore, we collect venue information by merging descriptions and tips and then generate tags of each venue, which are processed using keyword extraction approaches. Then we apply the recommendation algorithm with user’s initial preference and obtain the final integrate diffusion results for each user, recommending top-N venues by descending order. We conduct experiments on Foursquare datasets of two cities, the results on both datasets show that our recommender system can produce better performance, providing more personalized and higher novel recommendations.