Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7117041 | The Journal of China Universities of Posts and Telecommunications | 2016 | 12 Pages |
Abstract
With the rapid development of location-based networks, point-of-interest (POI) recommendation has become an important means to help people discover interesting and attractive locations, especially when users travel out of town. However, because users only check-in interaction is highly sparse, which creates a big challenge for POI recommendation. To tackle this challenge, we propose a joint probabilistic generative model called geographical temporal social content popularity (GTSCP) to imitate user check-in activities in a process of decision making, which effectively integrates the geographical influence, temporal effect, social correlation, content information and popularity impact factors to overcome the data sparsity, especially for out-of-town users. Our proposed the GTSCP supports two recommendation scenarios in a joint model, i.e., home-town recommendation and out-of-town recommendation. Experimental results show that GTSCP achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.
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Authors
Ren Xingyi, Song Meina, E Haihong, Song Junde,