Travel Recommendation via Author Topic Model Based Collaborative Filtering

Abstract

While automatic travel recommendation has attracted a lot of attentions, the existing approaches generally suffer from different kinds of weaknesses. For example, sparsity problem can significantly degrade the performance of traditional collaborative filtering (CF). If a user only visits very few locations, accurate similar user identification becomes very challenging due to lack of sufficient information. Motivated by this concern, we propose an Author Topic Collaborative Filtering (ATCF) method to facilitate comprehensive Points of Interest (POIs) recommendation for social media users. In our approach, the topics about user preference (e.g., cultural, cityscape, or landmark) are extracted from the textual description of photos by author topic model instead of from GPS (geo-tag). Consequently, unlike CF based approaches, even without GPS records, similar users could still be identified accurately according to the similarity of users’ topic preferences. In addition, ATCF doesn’t pre-define the category of travel topics. The category and user topic preference could be elicited simultaneously. Experiment results with a large test collection demonstrate various kinds of advantages of our approach.