Geographical Relevance Model For Long Tail Point-Of-Interest Recommendation

Point-of-Interest (POI) recommendation plays a key role in people's daily life, and has been widely studied in recent years, due to its increasingly applications (e.g., recommending new restaurants for users). One of important phenomena in the POI recommendation community is the data sparsity, which makes deep impact on the quality of recommendation. Existing works have proposed various models to alleviate the bottleneck of the data sparsity, and most of these works addressed this issue from the user perspective. To the best of our knowledge, few attention has been made to address this issue from the POI perspective. In this paper, we observe that the "long tail" POIs, which have few checkins and have less opportunity to be exposed, take up a great proportion among all the POIs. It is interesting and meaningful to investigate the long tail POI recommendation from the POI perspective. To this end, this paper proposes a new model, named GRM (geographical relevance model), that expands POI profiles via relevant POIs and employs the geographical information, addressing the limitations of existing models. Experimental results based on two public datasets demonstrate that our model is effective and competitive. It outperforms state-of-the-art models for the long tail POI recommendation problem.