A survey of collaborative filtering techniques
Reads0
Chats0
TLDR
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
More filters
Proceedings ArticleDOI
Low-rank singular value thresholding for recovering missing air quality data
TL;DR: The proposed SVT methodology can effectively recover missing air quality data, and outperform the existing Interpolation, and the parameter sensitivity of SVT is investigated.
Journal ArticleDOI
LORI: A Learning-to-Rank-Based Integration Method of Location Recommendation
TL;DR: A novel integration method is proposed: learning-to-rank-based integration, where a confidence coefficient is applied for each user in the integration process, and these coefficients can well optimize recommendation performance.
Proceedings ArticleDOI
Hybrid collaborative filtering model for improved recommendation
TL;DR: A new hybrid collaborative filtering model is proposed by combining item-based CF algorithm and user-basedCF algorithm, which is more robust to sparse problem and experimental results show the superiority of this approach over current state of the art methods.
Journal ArticleDOI
Exploiting social tags to overcome cold start recommendation problem
TL;DR: This study presents a collaborative filtering approach based on the expansion of users’ tags that outperforms the state-of-the-art tag-aware collaborative filtering approaches in terms of recommendation quality particularly in the cold-start situation.
Journal ArticleDOI
A personalized multimedia contents recommendation using a psychological model
TL;DR: A personal content manager which acts between content providers and mobile devices is defined and a method for recommending multimedia content in this manager, based on user's personal characteristic and preference, is proposed.
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.