MH Aghdam, M Analoui… - Journal of Information …, 2017 - journals.sagepub.com
Collaborative filtering is a popular strategy in recommender systems area. This approach gathers users' ratings and then predicts what users will rate based on their similarity to other …
Y Takama, H Shibata, Y Shiraishi - Journal of Advanced …, 2020 - jstage.jst.go.jp
This paper proposes a matrix-based collaborative filtering (CF) employing personal values (MCFPV). Introduction of various factors such as diversity and long-tailedness in addition to …
W Weijie, Y Jing, H Liang - 2012 International Conference on …, 2012 - ieeexplore.ieee.org
Many of the recent algorithms have been developed to improve the various aspects of collaborative filtering recommender systems, however, most of them do not take the …
Matrix Factorization (MF) is one of the most popular techniques used in Collaborative Filtering (CF) based Recommender System (RS). Most of the MF methods tend to remove …
A Mashhoori, S Hashemi - … Information and Database Systems: 4th Asian …, 2012 - Springer
Matrix factorization (MF) is one of the well-known methods in collaborative filtering to build accurate and efficient recommender systems. While in all the previous studies about MF …
L Ren, J Gu, W Xia - Energy Procedia, 2011 - infona.pl
Item-based collaborative filtering has been widely used in practice and is becoming the most promising approach in recommender systems. It predicts a user's interest for a target item …
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 …
Memory-based methods for collaborative filtering predict new ratings by averaging (weighted) ratings between, respectively, pairs of similar users or items. In practice, a large …
In recommender systems, Collaborative Filtering (CF) plays an essential role in promoting recommendation services. The conventional CF approach has limitations, namely data …