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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Attribute mapping and autoencoder neural network based matrix factorization initialization for recommendation systems
TL;DR: A hybrid initialization method based on attribute mapping and autoencoder neural network to solve the problems of SVD random initialization and achieve better performance than SVD random initialization and also be adopted to other matrix factorization methods.
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A new collaborative filtering algorithm using K-means clustering and neighbors' voting
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Hotel recommendation based on user preference analysis
TL;DR: A novel hotel recommendation framework is proposed that combines collaboration filtering (CF) with content-based (CBF) method to overcome sparsity issue, while ensuring high accuracy.
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Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey
TL;DR: This survey paper presents a detailed literature review of Context-aware Recommender Systems and approaches to improving performance for large scale datasets and the impact of incorporating contextual information on the quality and accuracy of the recommendation.
Proceedings ArticleDOI
Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization
TL;DR: This article proposed an enhanced graph learning network EGLN approach for collaborative filtering via mutual information maximization (EGLN) to better learn enhanced graph structure for CF and designed a local-global consistency optimization function to capture the global properties of the adaptive graph learning process.
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