Chen Cheng Haiqin Yang Irwin King Michael R. Lyu Fused Matrix Factorization with Geographical and Social Influence in Location-based Social Networks Chen Cheng Haiqin Yang Irwin King Michael R. Lyu {ccheng,hqyang,king,lyu}@cse.cuhk.edu.hk Introduction Check-in becomes a life style. How to provide accurate POI Recommendation in LBSNs? Contributions: Understand characteristics of LBSNs data Propose Multi-center Gaussian Model to capture geographical influence Fuse matrix factorization with geographical influence to enhance POI recommendation Check-in Data Characteristics Dataset Statistics: Crawled from Gowalla from Feb. 2009 to Sep. 2011 Remove users less than 10 check-ins and locations less than 20 visits Density is Multi-center and Normal Distribution: Check in around several centers Normal distribution to model human movement Inverse Distance Rule: Inversely proportion relationship between check-in prob. and distance Friendship Influence: Low common check-in rate: 9.8% on average, 38% no common Limited influence in POI recommendation Frequency Distribution: Highly skewed check-in frequency: 74% locations visited once, 3% visited more than 10 times Top-20 check-ins account for 80.5% of all check-ins (Pareto principle). Model Multi-center Gaussian Model: The prob. of visiting a location l is defined as: Centers are found by using greedy algorithm. Fused Framework: PMF: Model user preference MGM: Model geo-influence FMFMGM: Fused PMF and MGM normalized effect of check-in frequency Results Performance on Different Users: Summary Our proposed fused framework outperforms other state-of-the-art methods at least 50%.