Recommender systems collect various kinds of data to create their recommendations. Collaborative filtering is a common technique in this area. This technique gathers and …
Recommender systems actively collect various kinds of data in order to generate their recommendations. Collaborative filtering is based on collecting and analyzing information …
G Chen, F Wang, C Zhang - Information Processing & Management, 2009 - Elsevier
Collaborative filtering aims at predicting a test user's ratings for new items by integrating other like-minded users' rating information. The key assumption is that users sharing the …
Recommender systems are essential tools for many e-commerce services, such as Amazon, Netflix, etc. to recommend new items to users. Among various recommendation techniques …
M Jalili - International Journal of System Modeling and …, 2017 - zelusinternational.com
Abstract— Recommender systems are often used to provide useful recommendations for users. They use previous history of the users-items interactions, eg purchase history and/or …
In designing modern recommender systems, item feature information (or side information) is often ignored as most models focus on exploiting rating information. However, the side …
E Zhong, W Fan, Q Yang - Proceedings of the 2012 SIAM International …, 2012 - SIAM
Matrix factorization (MF) has been demonstrated to be one of the most competitive techniques for collaborative filtering. However, state-of-the-art MFs do not consider …
D kumar Bokde, S Girase… - CoRR, abs …, 2015 - researchgate.net
ABSTRACT Recommendation Systems apply Information Retrieval techniques to select the online information relevant to a given user. Collaborative Filtering (CF) is currently most …
C Feng, J Liang, P Song, Z Wang - Information Sciences, 2020 - Elsevier
Collaborative filtering is a fundamental technique in recommender systems, for which memory-based and matrix-factorization-based collaborative filtering are the two types of …