• Publications
  • Influence
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.
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.
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.
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 by capturing the geographical influence via modeling the probability of a user’s check-in on a location as a Multi-center Gaussian Model (MGM).
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.
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.
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.
Drain: An Online Log Parsing Approach with Fixed Depth Tree
TLDR
This work proposes an online log parsing method, namely Drain, that can parse logs in a streaming and timely manner, and uses a fixed depth parse tree, which encodes specially designed rules for parsing.
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.
Experience Report: System Log Analysis for Anomaly Detection
TLDR
A detailed review and evaluation of six state-of-the-art log-based anomaly detection methods, including three supervised methods and three unsupervised methods, and also releases an open-source toolkit allowing ease of reuse.
...
...