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The popularity of location-based social networks (LBSNs) has led to atremendous amount of user check-in data. Recommending points of interest (POIs)plays a key role in satisfying users' needs in LBSNs. While recent work hasexplored the idea of adopting collaborative ranking (CR) for recommendation,there have been few attempts to incorporate temporal information for POIrecommendation using CR. In this article, we propose a two-phase CR algorithmthat incorporates the geographical influence of POIs and is regularized basedon the variance of POIs popularity and users' activities over time. Thetime-sensitive regularizer penalizes user and POIs that have been moretime-sensitive in the past, helping the model to account for their long-termbehavioral patterns while learning from user-POI interactions. Moreover, in thefirst phase, it attempts to rank visited POIs higher than the unvisited ones,and at the same time, apply the geographical influence. In the second phase,our algorithm tries to rank users' favorite POIs higher on the recommendationlist. Both phases employ a collaborative learning strategy that enables themodel to capture complex latent associations from two different perspectives.Experiments on real-world datasets show that our proposed time-sensitivecollaborative ranking model beats state-of-the-art POI recommendation methods.

A Joint Two-Phase Time-Sensitive Regularized Collaborative Ranking Model for Point of Interest Recommendation