GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Less is more: probabilistic models for retrieving fewer relevant documents
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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
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
Structured learning for non-smooth ranking losses
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
One-Class Collaborative Filtering
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Regression-based latent factor models
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
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
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
RECON: a reciprocal recommender for online dating
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
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
OrdRec: an ordinal model for predicting personalized item rating distributions
Proceedings of the fifth ACM conference on Recommender systems
Item popularity and recommendation accuracy
Proceedings of the fifth ACM conference on Recommender systems
MyMediaLite: a free recommender system library
Proceedings of the fifth ACM conference on Recommender systems
Proceedings of the fifth ACM international conference on Web search and data mining
TFMAP: optimizing MAP for top-n context-aware recommendation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Unifying rating-oriented and ranking-oriented collaborative filtering for improved recommendation
Information Sciences: an International Journal
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
Big & personal: data and models behind netflix recommendations
Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
GAPfm: optimal top-n recommendations for graded relevance domains
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Top-N recommendations from implicit feedback leveraging linked open data
Proceedings of the 7th ACM conference on Recommender systems
Nonlinear latent factorization by embedding multiple user interests
Proceedings of the 7th ACM conference on Recommender systems
Learning to rank recommendations with the k-order statistic loss
Proceedings of the 7th ACM conference on Recommender systems
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
Improving pairwise learning for item recommendation from implicit feedback
Proceedings of the 7th ACM international conference on Web search and data mining
Proceedings of the 23rd international conference on World wide web
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In this paper we tackle the problem of recommendation in the scenarios with binary relevance data, when only a few (k) items are recommended to individual users. Past work on Collaborative Filtering (CF) has either not addressed the ranking problem for binary relevance datasets, or not specifically focused on improving top-k recommendations. To solve the problem we propose a new CF approach, Collaborative Less-is-More Filtering (CLiMF). In CLiMF the model parameters are learned by directly maximizing the Mean Reciprocal Rank (MRR), which is a well-known information retrieval metric for measuring the performance of top-k recommendations. We achieve linear computational complexity by introducing a lower bound of the smoothed reciprocal rank metric. Experiments on two social network datasets demonstrate the effectiveness and the scalability of CLiMF, and show that CLiMF significantly outperforms a naive baseline and two state-of-the-art CF methods.