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
Personalized paper recommendation in online social scholar system
TL;DR: This paper presents a practical paper recommender system, which aims to provide personalized research paper recommendations to users within an online social scholar system, and employs a supervised learning to rank approach to solve the problem.
Journal ArticleDOI
Prediction of Uterine Contractions Using Knowledge-Assisted Sequential Pattern Analysis
TL;DR: A knowledge-assisted sequential pattern analysis framework is designed to predict the intrauterine pressure in real time; anticipate the next contraction; and develop a sequential association rule mining approach to identify the patterns of the contractions from historical patient tracings (HT).
Proceedings ArticleDOI
A generic graph-based multidimensional recommendation framework and its implementations
TL;DR: This paper takes a graph-based approach to accomplishing multidimensional recommendation requirements in recommender systems and presents a generic graph- based multiddimensional recommendation framework.
Proceedings ArticleDOI
Collaborative filtering based online recommendation systems: A survey
TL;DR: This paper presents a survey of various state of the art techniques for recommendation systems and highlights the best techniques to generate accurate results.
Proceedings ArticleDOI
Big data based retail recommender system of non E-commerce
Chen Sun,Rong Gao,Hongsheng Xi +2 more
TL;DR: Experimental results show that the retail recommender model based on collaborative filtering is effective for the estimation of retail sales for each store and product and the corresponding distributed computing algorithm on MapReduce helps the system do scalable data processing easily.
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.