Learning geographical preferences for point-of-interest recommendation

• DOI: 10.1145/2487575.2487673
• Publication Date: 2013-08-13T12:31:21Z
• Authors: Bin Liu; Yanjie Fu; Zijun Yao; Hui Xiong
• Keywords: Margin (machine learning); Point of interest; Factor (programming language)

The problem of point interest (POI) recommendation is to provide personalized recommendations places interests, such as restaurants, for mobile users. Due its complexity and connection location based social networks (LBSNs), the decision process a user choose POI complex can be influenced by various factors, preferences, geographical influences, mobility behaviors. While there are some studies on recommendations, it lacks integrated analysis joint effect multiple factors. To this end, in paper, we propose novel probabilistic factor framework which strategically takes factors into consideration. Specifically, allows capture influences user's check-in behavior. Also, behaviors effectively exploited model. Moreover, model make use count data implicity feedback modeling preferences. Finally, experimental results real-world LBSNs show that proposed method outperforms state-of-the-art latent models with significant margin.