Loc2Vec-Based Cluster-Level Transition Behavior Mining for Successive POI Recommendation

Point of interest (POI) recommendation is a significant task in location-based social networks (LBSNs) as it can help to suggest new locations and makes LBSNs more prevalent to users. Successive POI recommendation is a nature extension of the general POI recommendation which utilizes users’ current state like the latest check-in records or timestamps to recommend subsequent POIs. Successive POI recommendation requires a well-constructed model for the transition patterns in POI sequences; however, existing works still have some limitations: 1) transitions are modeled on a relatively low level which cannot reflect users’ real intentions hidden behind; 2) there lacks a balance between the transition patterns modeled globally and personally; and 3) most works only consider the correlations between adjacent check-ins, but longer dependencies should be captured as well. To resolve the above issues, we present a successive POI recommendation approach called TTR which is based on the personalized transition pattern analysis on the cluster level for the check-in data in LBSN. It first clusters the POIs based on their representation vectors learnt from Word2Vec model, then it models users’ transition behavior on the cluster level through the additive Markov chain model, finally it recommends successive POIs based on a combination of personalized and global strategy. We conduct several experiments on the real datasets Gowalla and Brightkite to evaluate the performance of TTR, and the results show that the proposed method outperforms existing works in terms of precision and recall metrics, and the personalized strategy shows better performance while the global strategy can provide better diversity.