J
Jia-Dong Zhang
Researcher at City University of Hong Kong
Publications - 42
Citations - 1735
Jia-Dong Zhang is an academic researcher from City University of Hong Kong. The author has contributed to research in topics: Collaborative filtering & Recommender system. The author has an hindex of 17, co-authored 40 publications receiving 1390 citations. Previous affiliations of Jia-Dong Zhang include University of Hong Kong.
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Proceedings ArticleDOI
GeoSoCa: Exploiting Geographical, Social and Categorical Correlations for Point-of-Interest Recommendations
Jia-Dong Zhang,Chi-Yin Chow +1 more
TL;DR: A new POI recommendation approach called GeoSoCa is proposed through exploiting geographical correlations, social correlations and categorical correlations among users and POIs to achieve significantly superior recommendation quality compared to other state-of-the-artPOI recommendation techniques.
Proceedings ArticleDOI
iGSLR: personalized geo-social location recommendation: a kernel density estimation approach
Jia-Dong Zhang,Chi-Yin Chow +1 more
TL;DR: Experimental results show that iGSLR provides significantly superior location recommendation compared to other state-of-the-art geo-social recommendation techniques.
Proceedings ArticleDOI
LORE: exploiting sequential influence for location recommendations
TL;DR: A new approach called LORE is proposed to exploit sequential influence on location recommendations and achieves significantly superior location recommendations compared to other state-of-the-art recommendation techniques.
Journal ArticleDOI
iGeoRec: A Personalized and Efficient Geographical Location Recommendation Framework
TL;DR: A probabilistic approach to personalize the geographical influence as a personal distribution for each user and predict the probability of a user visiting any new location using her personal distribution is proposed.
Journal ArticleDOI
CoRe: Exploiting the personalized influence of two-dimensional geographic coordinates for location recommendations
Jia-Dong Zhang,Chi-Yin Chow +1 more
TL;DR: Experimental results show that CoRe achieves significantly superior performance compared to other state-of-the-art geo-social recommendation techniques.