Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
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
Collaborative filtering with privacy via factor analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Using SVD and demographic data for the enhancement of generalized Collaborative Filtering
Information Sciences: an International Journal
Restricted Boltzmann machines for collaborative filtering
Proceedings of the 24th international conference on Machine learning
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative prediction and ranking with non-random missing data
Proceedings of the third ACM conference on Recommender systems
Utilizing recommender systems to support software requirements elicitation
Proceedings of the 2nd International Workshop on Recommendation Systems for Software Engineering
Combining predictions for accurate recommender systems
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering via group-structured dictionary learning
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Collaborative filtering via temporal euclidean embedding
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Neural Networks
Leveraging tagging for neighborhood-aware probabilistic matrix factorization
Proceedings of the 21st ACM international conference on Information and knowledge management
Stochastic gradient descent with GPGPU
KI'12 Proceedings of the 35th Annual German conference on Advances in Artificial Intelligence
A modified random walk framework for handling negative ratings and generating explanations
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
Addressing cold-start in app recommendation: latent user models constructed from twitter followers
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Bridging memory-based collaborative filtering and text retrieval
Information Retrieval
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Collaborative filtering (CF) approaches proved to be effective for recommender systems in predicting user preferences in item selection using known user ratings of items. This subfield of machine learning has gained a lot of popularity with the Netflix Prize competition started in October 2006. Two major approaches for this problem are matrix factorization (MF) and the neighbor based approach (NB). In this work, we propose various variants of MF and NB that can boost the performance of the usual ensemble based scheme. First, we investigate various regularization scenarios for MF. Second, we introduce two NB methods: one is based on correlation coefficients and the other on linear least squares. At the experimentation part, we show that the proposed approaches compare favorably with existing ones in terms of prediction accuracy and/or required training time. We present results of blending the proposed methods.