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
Learning User Dependencies for Recommendation
TL;DR: This paper proposes a novel recommendation method, named probabilistic relational matrix factorization (PRMF), which can automatically learn the dependencies between users to improve recommendation accuracy and proposes an alternating optimization algorithm to solve the optimization problem of PRMF.
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Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary Computation
TL;DR: This paper designs a personalized recommendation system considering the three conflicting objectives, i.e., the accuracy, diversity, and novelty, and presents a multiobjective personalized recommendation algorithm using extreme point guided evolutionary computation (called MOEA-EPG).
Journal ArticleDOI
An empirical study on the effect of data sparsity and data overlap on cross domain collaborative filtering performance
Hyunwoo Hwangbo,Yang Sok Kim +1 more
TL;DR: This result implies that the use of cross-domain recommenders do not guarantee performance improvement, rather that it is necessary to consider relevant factors carefully to achieve performance improvement when using cross- domain recommenders.
A Preliminary Study on a Recommender System for the Million Songs Dataset Challenge.
TL;DR: Memory-based collaborative filtering approaches are investigated on defining suitable similarity functions, studying the effect of the “locality” of the collaborative scoring function, and aggregating multiple ranking strategies to define the overall recommendation.
Proceedings ArticleDOI
Towards Online, Accurate, and Scalable QoS Prediction for Runtime Service Adaptation
TL;DR: This paper proposes a novel QoS prediction approach, namely adaptive matrix factorization (AMF), which is inspired from the collaborative filtering model used in recommender systems and extends conventional Matrix factorization into an online, accurate, and scalable model by employing techniques of data transformation, online learning, and adaptive weights.
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