• 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. 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
An Overview of Microsoft Academic Service (MAS) and Applications
TLDR
A knowledge driven, highly interactive dialog that seamlessly combines reactive search and proactive suggestion experience, and a proactive heterogeneous entity recommendation are demonstrated. Expand
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
TLDR
The NetMF method offers significant improvements over DeepWalk and LINE for conventional network mining tasks and provides the theoretical connections between skip-gram based network embedding algorithms and the theory of graph Laplacian. 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
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
GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs
TLDR
The effectiveness of GaAN on the inductive node classification problem is demonstrated, and the Graph Gated Recurrent Unit (GGRU) is constructed with GaAN as a building block to address the traffic speed forecasting problem. Expand
Effective missing data prediction for collaborative filtering
TLDR
This paper uses the enhanced Pearson Correlation Coefficient (PCC) algorithm by adding one parameter which overcomes the potential decrease of accuracy when computing the similarity of users or items and proposes an effective missing data prediction algorithm, in which information of both users and items is taken into account. Expand
An experimental study on implicit social recommendation
  • Hao Ma
  • Computer Science
  • SIGIR
  • 28 July 2013
TLDR
This study conducts comprehensive experimental analysis on three recommendation datasets and indicates that implicit user and item social information, including similar and dissimilar relationships, can be employed to improve traditional recommendation methods. Expand
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