Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
A support vector method for optimizing average precision
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
AdaRank: a boosting algorithm for information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
SoftRank: optimizing non-smooth rank metrics
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
EigenRank: a ranking-oriented approach to collaborative filtering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Directly optimizing evaluation measures in learning to rank
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Information Retrieval
Introduction to 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
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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
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Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Context-based splitting of item ratings in collaborative filtering
Proceedings of the third ACM conference on Recommender systems
Tensor Decompositions and Applications
SIAM Review
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Gradient descent optimization of smoothed information retrieval metrics
Information Retrieval
On statistical analysis and optimization of information retrieval effectiveness metrics
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
Proceedings of the fourth ACM conference on Recommender systems
One-Class Matrix Completion with Low-Density Factorizations
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Collaborative competitive filtering: learning recommender using context of user choice
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Fast context-aware recommendations with factorization machines
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Localized factor models for multi-context recommendation
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MyMediaLite: a free recommender system library
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CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering
Proceedings of the sixth ACM conference on Recommender systems
Unifying rating-oriented and ranking-oriented collaborative filtering for improved recommendation
Information Sciences: an International Journal
Proceedings of the 2013 international conference on Intelligent user interfaces
Mining large streams of user data for personalized recommendations
ACM SIGKDD Explorations Newsletter
Optimizing top-n collaborative filtering via dynamic negative item sampling
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Fine-grained preference-aware location search leveraging crowdsourced digital footprints from LBSNs
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
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Proceedings of the 21st ACM international conference on Multimedia
GAPfm: optimal top-n recommendations for graded relevance domains
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
xCLiMF: optimizing expected reciprocal rank for data with multiple levels of relevance
Proceedings of the 7th ACM conference on Recommender systems
Learning to rank for recommender systems
Proceedings of the 7th ACM conference on Recommender systems
A unified framework for reputation estimation in online rating systems
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Merging trust in collaborative filtering to alleviate data sparsity and cold start
Knowledge-Based Systems
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In this paper, we tackle the problem of top-N context-aware recommendation for implicit feedback scenarios. We frame this challenge as a ranking problem in collaborative filtering (CF). Much of the past work on CF has not focused on evaluation metrics that lead to good top-N recommendation lists in designing recommendation models. In addition, previous work on context-aware recommendation has mainly focused on explicit feedback data, i.e., ratings. We propose TFMAP, a model that directly maximizes Mean Average Precision with the aim of creating an optimally ranked list of items for individual users under a given context. TFMAP uses tensor factorization to model implicit feedback data (e.g., purchases, clicks) with contextual information. The optimization of MAP in a large data collection is computationally too complex to be tractable in practice. To address this computational bottleneck, we present a fast learning algorithm that exploits several intrinsic properties of average precision to improve the learning efficiency of TFMAP, and to ensure its scalability. We experimentally verify the effectiveness of the proposed fast learning algorithm, and demonstrate that TFMAP significantly outperforms state-of-the-art recommendation approaches.