Location-based social networks have been increasingly used to experience users new possibilities, including personalized point-ofinterest (POI) recommendation services which leverages on the overlapping of user trajectories to recommend POI collaboratively. POI recommendation is challenging as it does not just suffers from the problems known for collaborative filtering such as data sparsity and cold-start, but to a much greater extent. Most of the related works apply the conventional recommendation approaches to POI recommendation while overlooking the personalized time-variant human behavioral tendency. In this paper, we put forward a tensor factorization-based ranking methodology to recommend users their interested locations by considering their timevarying behavioral trends. We also propose to categorize the locations to address data sparsity and cold-start issues, and accordingly new locations the user have not been visited can thus be bubbled up during ranking the location candidates. The tensor factorization is carefully studied to prune the irrelevant factors to the ranking results to achieve efficient POI recommendation. The experimental results validate the effectiveness of our proposed mechanism which outperforms the state-of-the-art approaches by over 8% for precision.