Journal ArticleDOI
A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model
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TLDR
A novel technique for predicting the tastes of users in recommender systems based on collaborative filtering is presented, based on factorizing the rating matrix into two non negative matrices whose components lie within the range 0, 1 with an understandable probabilistic meaning.Abstract:
In this paper we present a novel technique for predicting the tastes of users in recommender systems based on collaborative filtering. Our technique is based on factorizing the rating matrix into two non negative matrices whose components lie within the range 0, 1 with an understandable probabilistic meaning. Thanks to this decomposition we can accurately predict the ratings of users, find out some groups of users with the same tastes, as well as justify and understand the recommendations our technique provides.read more
Citations
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Journal ArticleDOI
A graph regularized non-negative matrix factorization method for identifying microRNA-disease associations.
TL;DR: A new method with graph regularized non-negative matrix factorization in heterogeneous omics data, called GRNMF, to discover potential associations between miRNAs and diseases with higher accuracy compared with other recent approaches is proposed.
Journal ArticleDOI
A Survey of Collaborative Filtering-Based Recommender Systems: From Traditional Methods to Hybrid Methods Based on Social Networks
TL;DR: The recent hybrid CF-based recommendation techniques fusing social networks to solve data sparsity and high dimensionality are introduced and provide a novel point of view to improve the performance of RS, thereby presenting a useful resource in the state-of-the-art research result for future researchers.
Journal ArticleDOI
Recommender Systems for Large-Scale Social Networks: A review of challenges and solutions
TL;DR: This work reviews the various facets of large-scale social recommender systems, summarizing the challenges and interesting problems and discussing some of the solutions.
Journal ArticleDOI
A Recommendation Model Based on Deep Neural Network
TL;DR: A model combining a collaborative filtering recommendation algorithm with deep learning technology is proposed, therein consisting of two parts that obtains the latent features more accurately by improving upon the traditional matrix factorization algorithm.
References
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Book
Convex Optimization
Stephen Boyd,Lieven Vandenberghe +1 more
TL;DR: In this article, the focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them, and a comprehensive introduction to the subject is given. But the focus of this book is not on the optimization problem itself, but on the problem of finding the appropriate technique to solve it.
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Latent dirichlet allocation
TL;DR: This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.
Proceedings Article
Latent Dirichlet Allocation
TL;DR: This paper proposed a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI).
Journal ArticleDOI
Indexing by Latent Semantic Analysis
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Journal ArticleDOI
Learning the parts of objects by non-negative matrix factorization
TL;DR: An algorithm for non-negative matrix factorization is demonstrated that is able to learn parts of faces and semantic features of text and is in contrast to other methods that learn holistic, not parts-based, representations.