Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
TV Program Recommendation for Multiple Viewers Based on user Profile Merging
User Modeling and User-Adapted Interaction
Adapting ranking SVM to document retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Group modeling in a public space: methods, techniques, experiences
AIC'05 Proceedings of the 5th WSEAS International Conference on Applied Informatics and Communications
EigenRank: a ranking-oriented approach to collaborative filtering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
One-Class Collaborative Filtering
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploiting user similarity based on rated-item pools for improved user-based collaborative filtering
Proceedings of the third ACM conference on Recommender systems
Probabilistic latent preference analysis for collaborative filtering
Proceedings of the 18th ACM conference on Information and knowledge management
The adaptive web
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Group recommendations with rank aggregation and collaborative filtering
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
New approaches to mood-based hybrid collaborative filtering
Proceedings of the Workshop on Context-Aware Movie Recommendation
Mining mood-specific movie similarity with matrix factorization for context-aware recommendation
Proceedings of the Workshop on Context-Aware Movie Recommendation
Movie recommendations based in explicit and implicit features extracted from the Filmtipset dataset
Proceedings of the Workshop on Context-Aware Movie Recommendation
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Evaluation of group profiling strategies
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Group recommendation in context
Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation
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In this paper, we describe our solutions to the first track of CAMRa2011 challenge. The goal of this track is to generate a movie ranking list for each household. To achieve this goal, we propose to use the ranking oriented matrix factorization and the matrix factorization with negative examples sampling. We also adopt feature-based matrix factorization framework to incorporate various contextual information to our model, including user-household relations, item neighborhood, user implicit feedback, etc. Finally, we elaborate two kinds of methods to recommend movies for each household based on our models. Experimental results show that our proposed approaches achieve significant improvement over baseline methods.