C
Chen Cheng
Researcher at The Chinese University of Hong Kong
Publications - 6
Citations - 1215
Chen Cheng is an academic researcher from The Chinese University of Hong Kong. The author has contributed to research in topics: Recommender system & Context (language use). The author has an hindex of 4, co-authored 6 publications receiving 1072 citations.
Papers
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Proceedings Article
Fused matrix factorization with geographical and social influence in location-based social networks
TL;DR: This paper is the first to fuse MF with geographical and social influence for POI recommendation in LBSNs via modeling the probability of a user's check-in on a location as a Multicenter Gaussian Model (MGM) and fuse the geographical influence into a generalized matrix factorization framework.
Proceedings Article
Where you like to go next: successive point-of-interest recommendation
TL;DR: This paper proposes a novel matrix factorization method, namely FPMC-LR, to embed the personalized Markov chains and the localized regions in the check-in sequence, and utilizes the information of localized regions to boost recommendation.
Proceedings ArticleDOI
Gradient boosting factorization machines
TL;DR: A novel Gradient Boosting Factorization Machine (GBFM) model is proposed to incorporate feature selection algorithm with Factorization Machines into a unified framework and the efficiency and effectiveness of the algorithm compared to other state-of-the-art methods are demonstrated.
Journal ArticleDOI
A Unified Point-of-Interest Recommendation Framework in Location-Based Social Networks
TL;DR: A unified POI recommendation framework is proposed, which unifies users’ preferences, geographical influence and personalized ranking, and shows that the results on both datasets show that the proposed framework can produce better performance.
Patent
Characteristic recommendation method and device
TL;DR: In this paper, the authors proposed a method for automatic selection of the effective combined characteristics, which are time-saving and labor-saving, effectively solve the difficult problems of time waste and labor waste in the existing manual characteristic selecting process and can improve effectiveness of a recommendation system.