Irwin King

The Chinese University of Hong Kong
0000-0001-8106-6447
http://www.cse.cuhk.edu.hk/irwin.king
Publications418
h-index62
Citations16,794
Highly Influential Citations1,623
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Recommender systems with social regularization
TLDR
This paper proposes a matrix factorization framework with social regularization, which can be easily extended to incorporate other contextual information, like social tags, etc, and demonstrates that the approaches outperform other state-of-the-art methods. Expand
SoRec: social recommendation using probabilistic matrix factorization
TLDR
A factor analysis approach based on probabilistic matrix factorization to solve the data sparsity and poor prediction accuracy problems by employing both users' social network information and rating records is proposed. Expand
Learning to recommend with social trust ensemble
TLDR
This work proposes a novel probabilistic factor analysis framework, which naturally fuses the users' tastes and their trusted friends' favors together and coin the term Social Trust Ensemble to represent the formulation of the social trust restrictions on the recommender systems. Expand
Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks
TLDR
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. Expand
QoS-Aware Web Service Recommendation by Collaborative Filtering
TLDR
This paper proposes a collaborative filtering approach for predicting QoS values of Web services and making Web service recommendation by taking advantages of past usage experiences of service users, and shows that the algorithm achieves better prediction accuracy than other approaches. Expand
Where You Like to Go Next: Successive Point-of-Interest Recommendation
TLDR
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. Expand
WSRec: A Collaborative Filtering Based Web Service Recommender System
TLDR
The comprehensive experimental analysis shows that WSRec achieves better prediction accuracy than other approaches, and includes a user-contribution mechanism for Web service QoS information collection and an effective and novel hybrid collaborative filtering algorithm for Web Service QoS value prediction. Expand
Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix Factorization
TLDR
This paper proposes a collaborative quality-of-service (QoS) prediction approach for web services by taking advantages of the past web service usage experiences of service users, and achieves higher prediction accuracy than other approaches. Expand
Leveraging Social Connections to Improve Personalized Ranking for Collaborative Filtering
TLDR
A model, SBPR (Social Bayesian Personalized Ranking), is developed based on the simple observation that users tend to assign higher ranks to items that their friends prefer, and it is shown that SBPR outperforms alternatives in ranking prediction both in warm- and cold-start settings. Expand
Simple and Efficient Multiple Kernel Learning by Group Lasso
TLDR
This paper forms a closed-form solution for optimizing the kernel weights based on the equivalence between group-lasso and MKL that leads to an efficient algorithm for MKL, but also generalizes to the case for Lp-MKL. Expand
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