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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Personalized travel route recommendation using collaborative filtering based on GPS trajectories
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A k-anonymous approach to privacy preserving collaborative filtering
TL;DR: A new technique for Privacy Preserving Collaborative Filtering (PPCF) based on microaggregation is proposed, which provides accurate recommendations estimated from perturbed data whilst guaranteeing user k -anonymity.
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A Path-constrained Framework for Discriminating Substitutable and Complementary Products in E-commerce
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Proceedings ArticleDOI
Dual Channel Hypergraph Collaborative Filtering
TL;DR: A dual channel hypergraph collaborative filtering framework is proposed and a dual channel learning strategy is introduced to learn the representation of users and items so that these two types of data can be elegantly interconnected while still maintaining their specific properties.
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A Web service QoS prediction approach based on time- and location-aware collaborative filtering
Chengyuan Yu,Linpeng Huang +1 more
TL;DR: Experimental results show that the proposed time-aware and location-aware CF algorithms are capable of addressing the three important challenges of recommender systems–high quality of prediction, high scalability, and easy to build and update.
References
More filters
Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Book
Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Journal ArticleDOI
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).
Some methods for classification and analysis of multivariate observations
TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.