GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
EigenRank: a ranking-oriented approach to collaborative filtering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Influence and correlation in social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to recommend with social trust ensemble
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Factor in the neighbors: Scalable and accurate collaborative filtering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Probabilistic latent preference analysis for collaborative filtering
Proceedings of the 18th ACM conference on Information and knowledge management
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Training and testing of recommender systems on data missing not at random
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Temporal recommendation on graphs via long- and short-term preference fusion
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the fourth ACM conference on Recommender systems
A matrix factorization technique with trust propagation for recommendation in social networks
Proceedings of the fourth ACM conference on Recommender systems
Adapting neighborhood and matrix factorization models for context aware recommendation
Proceedings of the Workshop on Context-Aware Movie Recommendation
Putting things in context: Challenge on Context-Aware Movie Recommendation
Proceedings of the Workshop on Context-Aware Movie Recommendation
Recommender systems with social regularization
Proceedings of the fourth ACM international conference on Web search and data mining
Collaborative filtering by personality diagnosis: a hybrid memory- and model-based approach
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
SoCo: a social network aided context-aware recommender system
Proceedings of the 22nd international conference on World Wide Web
GBPR: group preference based Bayesian personalized ranking for one-class collaborative filtering
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Most existing collaborative filtering models only consider the use of user feedback (e.g., ratings) and meta data (e.g., content, demographics). However, in most real world recommender systems, context information, such as time and social networks, are also very important factors that could be considered in order to produce more accurate recommendations. In this work, we address several challenges for the context aware movie recommendation tasks in CAMRa 2010: (1) how to combine multiple heterogeneous forms of user feedback? (2) how to cope with dynamic user and item characteristics? (3) how to capture and utilize social connections among users? For the first challenge, we propose a novel ranking based matrix factorization model to aggregate explicit and implicit user feedback. For the second challenge, we extend this model to a sequential matrix factorization model to enable time-aware parametrization. Finally, we introduce a network regularization function to constrain user parameters based on social connections. To the best of our knowledge, this is the first study that investigates the collective modeling of social and temporal dynamics. Experiments on the CAMRa 2010 dataset demonstrated clear improvements over many baselines.