A survey of collaborative filtering techniques
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From basic techniques to the state-of-the-art, this paper attempts to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.Abstract:
As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, modelbased, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.read more
Citations
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Proceedings ArticleDOI
Stylometric relevance-feedback towards a hybrid book recommendation algorithm
TL;DR: It is demonstrated that writing style influences book selection; that book content, characterized with writing style, can be used to improve collaborative filtering results; and that negative examples do not improve final predictions.
Journal ArticleDOI
A Novel Classification Framework for Evaluating Individual and Aggregate Diversity in Top-N Recommendations
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Expectation-Maximization collaborative filtering with explicit and implicit feedback
TL;DR: A novel method, Expectation-Maximization Collaborative Filtering (EMCF), based on matrix factorization, which combines explicit and implicit feedback together in EMCF to infer users' preferences by learning latent factor vectors from Matrix factorization.
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On the Case of Privacy in the IoT Ecosystem: A Survey
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Recommender Systems in Antiviral Drug Discovery
Ekaterina A. Sosnina,Sergey Sosnin,Anastasia A. Nikitina,Ivan Nazarov,Dmitry I. Osolodkin,Maxim V. Fedorov,Maxim V. Fedorov +6 more
TL;DR: RS methods for the prediction of the antiviral activity class (active/inactive) for compounds extracted from ChEMBL achieved relatively good prediction quality for binary classification of individual interactions and compound profiles, as quantified by cross-validation and external validation receiver operating characteristic (ROC) score.
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