Collaborative recommendation with multiclass preference context
Factorization-and neighborhood-based methods have been recognized as state-of-the-art
approaches for collaborative recommendation tasks. In this article, the authors take user …
approaches for collaborative recommendation tasks. In this article, the authors take user …
Contextual collaborative filtering via hierarchical matrix factorization
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 …
techniques for collaborative filtering. However, state-of-the-art MFs do not consider …
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 …
Filtering (CF) based Recommender System (RS). Most of the MF methods tend to remove …
Leveraging tagging for neighborhood-aware probabilistic matrix factorization
Collaborative Filtering (CF) is a popular way to build recommender systems and has been
successfully employed in many applications. Generally, two kinds of approaches to CF, the …
successfully employed in many applications. Generally, two kinds of approaches to CF, the …
Collaborative filtering recommendation based on all-weighted matrix factorization and fast optimization
H Li, X Diao, J Cao, Q Zheng - Ieee Access, 2018 - ieeexplore.ieee.org
Collaborative filtering recommendation with implicit feedbacks (eg, clicks, views, and plays)
is regarded as one of the most challenging issues in both academia and industry. From …
is regarded as one of the most challenging issues in both academia and industry. From …
Extending a tag-based collaborative recommender with co-occurring information interests
N Mauro, L Ardissono - Proceedings of the 27th ACM Conference on …, 2019 - dl.acm.org
Collaborative Filtering is largely applied to personalize item recommendation but its
performance is affected by the sparsity of rating data. In order to address this issue, recent …
performance is affected by the sparsity of rating data. In order to address this issue, recent …
Confidence-aware matrix factorization for recommender systems
Collaborative filtering (CF), particularly matrix factorization (MF) based methods, have been
widely used in recommender systems. The literature has reported that matrix factorization …
widely used in recommender systems. The literature has reported that matrix factorization …
Representation learning with pair-wise constraints for collaborative ranking
Last decades have witnessed a vast amount of interest and research in recommendation
systems. Collaborative filtering, which uses the known preferences of a group of users to …
systems. Collaborative filtering, which uses the known preferences of a group of users to …
Multi-domain collaborative recommendation with feature selection
L Liu, J Cui, W Song, H Wang - China Communications, 2017 - ieeexplore.ieee.org
Collaborative filtering, as one of the most popular techniques, plays an important role in
recommendation systems. However, when the user-item rating matrix is sparse, its …
recommendation systems. However, when the user-item rating matrix is sparse, its …
Comparative analysis of collaborative filtering techniques for the multi-criteria recommender systems
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 …
Netflix, etc. to recommend new items to users. Among various recommendation techniques …