Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
An introduction to the mathematical theory of inverse problems
An introduction to the mathematical theory of inverse problems
Parameter adaptation in stochastic optimization
On-line learning in neural networks
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
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
Local Gain Adaptation in Stochastic Gradient Descent
Local Gain Adaptation in Stochastic Gradient Descent
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Document clustering by concept factorization
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
An automatic weighting scheme for collaborative filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Knowledge and Data Engineering
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Using Singular Value Decomposition Approximation for Collaborative Filtering
CEC '05 Proceedings of the Seventh IEEE International Conference on E-Commerce Technology
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Using SVD and demographic data for the enhancement of generalized Collaborative Filtering
Information Sciences: an International Journal
Effective missing data prediction for collaborative filtering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Improving Recommender Systems by Incorporating Social Contextual Information
ACM Transactions on Information Systems (TOIS)
Learning bidirectional asymmetric similarity for collaborative filtering via matrix factorization
Data Mining and Knowledge Discovery
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Memory-based collaborative filtering aims at predicting the utility of a certain item for a particular user based on the previous ratings from similar users and similar items. Previous studies in finding similar users and items are based on user-defined similarity metrics such as Pearson Correlation Coefficient or Vector Space Similarity which are not adaptive and optimized for different applications and datasets. Moreover, previous studies have treated the similarity function calculation between users and items separately. In this paper, we propose a novel adaptive bidirectional similarity metricfor collaborative filtering. We automatically learn similarities between users and items simultaneously through matrix factorization. We show that our model naturally extends the memory based approaches. Theoretical analysis shows our model to be a novel generalization of the SVD model. We evaluate our method using three benchmark datasets, including MovieLens, EachMovie and Netflix, through which we show that our methods outperform many previous baselines.