M
Michael R. Lyu
Researcher at The Chinese University of Hong Kong
Publications - 716
Citations - 38284
Michael R. Lyu is an academic researcher from The Chinese University of Hong Kong. The author has contributed to research in topics: Software quality & Web service. The author has an hindex of 89, co-authored 696 publications receiving 33257 citations. Previous affiliations of Michael R. Lyu include City University of Hong Kong & Bell Labs.
Papers
More filters
Proceedings ArticleDOI
Recommender systems with social regularization
TL;DR: 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.
Proceedings ArticleDOI
SoRec: social recommendation using probabilistic matrix factorization
TL;DR: 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.
Book
Handbook of software reliability engineering
TL;DR: Technical foundations introduction software reliability and system reliability the operational profile software reliability modelling survey model evaluation and recalibration techniques practices and experiences and best current practice of SRE software reliability measurement experience.
Proceedings ArticleDOI
Learning to recommend with social trust ensemble
Hao Ma,Irwin King,Michael R. Lyu +2 more
TL;DR: 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.
Journal ArticleDOI
QoS-Aware Web Service Recommendation by Collaborative Filtering
TL;DR: 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.