A survey of collaborative filtering-based recommender systems: From traditional methods to hybrid methods based on social networks
R Chen, Q Hua, YS Chang, B Wang, L Zhang… - IEEE …, 2018 - ieeexplore.ieee.org
In the era of big data, recommender system (RS) has become an effective information
filtering tool that alleviates information overload for Web users. Collaborative filtering (CF) …
filtering tool that alleviates information overload for Web users. Collaborative filtering (CF) …
Recommender systems survey
Recommender systems have developed in parallel with the web. They were initially based
on demographic, content-based and collaborative filtering. Currently, these systems are …
on demographic, content-based and collaborative filtering. Currently, these systems are …
A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model
In this paper we present a novel technique for predicting the tastes of users in recommender
systems based on collaborative filtering. Our technique is based on factorizing the rating …
systems based on collaborative filtering. Our technique is based on factorizing the rating …
A reliable deep representation learning to improve trust-aware recommendation systems
Deep neural networks have been extensively employed in many applications such as
natural language processing and computer vision. They have attracted a lot of attention in …
natural language processing and computer vision. They have attracted a lot of attention in …
Alleviating data sparsity problem in time-aware recommender systems using a reliable rating profile enrichment approach
Recommender systems use intelligent algorithms to learn a user's preferences and provide
them relevant suggestions. Lack of sufficient ratings–also known as data sparsity problem …
them relevant suggestions. Lack of sufficient ratings–also known as data sparsity problem …
A reliability-based recommendation method to improve trust-aware recommender systems
P Moradi, S Ahmadian - Expert Systems with Applications, 2015 - Elsevier
Recommender systems (RSs) are programs that apply knowledge discovery techniques to
make personalized recommendations for user's information on the web. In online sharing …
make personalized recommendations for user's information on the web. In online sharing …
Simultaneous co-clustering and learning to address the cold start problem in recommender systems
ALV Pereira, ER Hruschka - Knowledge-Based Systems, 2015 - Elsevier
Abstract Recommender Systems (RSs) are powerful and popular tools for e-commerce. To
build their recommendations, RSs make use of varied data sources, which capture the …
build their recommendations, RSs make use of varied data sources, which capture the …
A social recommender system based on reliable implicit relationships
Recommender systems attempt to suggest information that is of potential interest to users
helping them to quickly find information relevant to them. In addition to historical user–item …
helping them to quickly find information relevant to them. In addition to historical user–item …
New insights towards developing recommender systems
Promoting recommender systems in real-world applications requires deep investigations
with emphasis on their next generation. This survey offers a comprehensive and systematic …
with emphasis on their next generation. This survey offers a comprehensive and systematic …
RDERL: Reliable deep ensemble reinforcement learning-based recommender system
Recommender systems (RSs) have been employed for many real-world applications
including search engines, social networks, and information retrieval systems as powerful …
including search engines, social networks, and information retrieval systems as powerful …