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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Journal ArticleDOI
An Approach for Building Efficient and Accurate Social Recommender Systems Using Individual Relationship Networks
TL;DR: This study proposes a new approach to manage the complexity of adding social relation networks to recommender systems by developing a novel fitting algorithm of relationship networks and fuse matrix factorization with social regularization and the neighborhood model using IRN's to generate recommendations.
Book ChapterDOI
Predicting User's Political Party Using Ideological Stances
TL;DR: This work exploits users' ideological stances on controversial issues to predict political party of online users and proposes a collaborative filtering approach to solve the data sparsity problem of users stances on ideological topics and applies clustering method to group the users with the same party.
Journal ArticleDOI
Domain-Sensitive Recommendation with User-Item Subgroup Analysis
TL;DR: A Domain-sensitive Recommendation (DsRec) algorithm, to make the rating prediction by exploring the user-item subgroup analysis simultaneously, in which a user- item subgroup is deemed as a domain consisting of a subset of items with similar attributes and a subsets of users who have interests in these items.
Journal ArticleDOI
CluCF: a clustering CF algorithm to address data sparsity problem
Chengyuan Yu,Linpeng Huang +1 more
TL;DR: This paper proposes a novel approach CluCF, which employs user clusters and service clusters to address the data sparsity problem and classifies the new user by location factor to lower the time complexity of updating clusters and shows that the approach is capable of alleviating the dataSparsity problem.
Collaborative Recommendation with User Generated Content
Yueshen Xua,Jianwei Yina +1 more
TL;DR: Wang et al. as mentioned in this paper proposed a unified way to utilize various types of user generated content (UGC) to enhance the recommendation accuracy, which can not only acquire prediction values of missing ratings, but also produce interpretable topics.
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