Ordinal consistency based matrix factorization model for exploiting side information in collaborative filtering

A Pujahari, DS Sisodia - Information Sciences, 2023 - Elsevier
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 …

Collaborative filtering using non-negative matrix factorisation

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 …

Matrix-based collaborative filtering employing personal values-based modeling and model relationship learning

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 …

An improved collaborative filtering based on item similarity modified and common ratings

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 …

Pair-wise preference relation based probabilistic matrix factorization for collaborative filtering in recommender system

A Pujahari, DS Sisodia - Knowledge-Based Systems, 2020 - Elsevier
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 …

Incorporating hierarchical information into the matrix factorization model for collaborative filtering

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 …

A weighted similarity-boosted collaborative filtering approach

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 …

Comparative analysis of collaborative filtering techniques for the multi-criteria recommender systems

R Singh, P Dwivedi, V Kant - Multimedia Tools and Applications, 2024 - Springer
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 …

Unifying user-based and item-based collaborative filtering approaches by similarity fusion

J Wang, AP De Vries, MJT Reinders - Proceedings of the 29th annual …, 2006 - dl.acm.org
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 …

Rec-CFSVD: Implementing Recommendation System Using Collaborative Filtering and Singular Value Decomposition (SVD)

T Anwar, V Uma, G Srivastava - International Journal of Information …, 2021 - World Scientific
In recommender systems, Collaborative Filtering (CF) plays an essential role in promoting
recommendation services. The conventional CF approach has limitations, namely data …