1. 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
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 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%.