An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
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)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Latent semantic analysis for multiple-type interrelated data objects
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Using mixture models for collaborative filtering
Journal of Computer and System Sciences
Tag-aware recommender systems by fusion of collaborative filtering algorithms
Proceedings of the 2008 ACM symposium on Applied computing
EigenRank: a ranking-oriented approach to collaborative filtering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Improving maximum margin matrix factorization
Machine Learning
Relational learning via collective matrix factorization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
From Web to Social Web: Discovering and Deploying User and Content Profiles
Tag recommendations based on tensor dimensionality reduction
Proceedings of the 2008 ACM conference on Recommender systems
SoRec: social recommendation using probabilistic matrix factorization
Proceedings of the 17th ACM conference on Information and knowledge management
Effective latent space graph-based re-ranking model with global consistency
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Scalable Tensor Decompositions for Multi-aspect Data Mining
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
Learning optimal ranking with tensor factorization for tag recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
On social networks and collaborative recommendation
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Exploiting user similarity based on rated-item pools for improved user-based collaborative filtering
Proceedings of the third ACM conference on Recommender systems
Learning to recommend with trust and distrust relationships
Proceedings of the third ACM conference on Recommender systems
Context-based splitting of item ratings in 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
IEEE Transactions on Knowledge and Data Engineering
Pairwise interaction tensor factorization for personalized tag recommendation
Proceedings of the third ACM international conference on Web search and data mining
Music Recommendation Using Content and Context Information Mining
IEEE Intelligent Systems
A Survey of Accuracy Evaluation Metrics of Recommendation Tasks
The Journal of Machine Learning Research
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Proceedings of the fourth ACM conference on Recommender systems
List-wise learning to rank with matrix factorization for 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
Factorization models for context-/time-aware movie recommendations
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
Putting things in context: Challenge on Context-Aware Movie Recommendation
Proceedings of the Workshop on Context-Aware Movie Recommendation
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Context-aware recommendation seeks to improve recommendation performance by exploiting various information sources in addition to the conventional user-item matrix used by recommender systems. We propose a novel context-aware movie recommendation algorithm based on joint matrix factorization (JMF). We jointly factorize the user-item matrix containing general movie ratings and other contextual movie similarity matrices to integrate contextual information into the recommendation process. The algorithm was developed within the scope of the mood-aware recommendation task that was offered by the Moviepilot mood track of the 2010 context-aware movie recommendation (CAMRa) challenge. Although the algorithm could generalize to other types of contextual information, in this work, we focus on two: movie mood tags and movie plot keywords. Since the objective in this challenge track is to recommend movies for a user given a specified mood, we devise a novel mood-specific movie similarity measure for this purpose. We enhance the recommendation based on this measure by also deploying the second movie similarity measure proposed in this article that takes into account the movie plot keywords. We validate the effectiveness of the proposed JMF algorithm with respect to the recommendation performance by carrying out experiments on the Moviepilot challenge dataset. We demonstrate that exploiting contextual information in JMF leads to significant improvement over several state-of-the-art approaches that generate movie recommendations without using contextual information. We also demonstrate that our proposed mood-specific movie similarity is better suited for the task than the conventional mood-based movie similarity measures. Finally, we show that the enhancement provided by the movie similarity capturing the plot keywords is particularly helpful in improving the recommendation to those users who are significantly more active in rating the movies than other users.