A deep learning based trust-and tag-aware recommender system
Recommender systems are popular tools used in many applications, such as e-commerce, e-
learning, and social networks to help users select their desired items. Collaborative filtering …
learning, and social networks to help users select their desired items. Collaborative filtering …
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 …
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 …
Social collaborative filtering by trust
Recommender systems are used to accurately and actively provide users with potentially
interesting information or services. Collaborative filtering is a widely adopted approach to …
interesting information or services. Collaborative filtering is a widely adopted approach to …
Enhancing collaborative recommendation performance by combining user preference and trust-distrust propagation in social networks
WP Lee, CY Ma - Knowledge-Based Systems, 2016 - Elsevier
Collaborative filtering (CF) is one of the most popular recommendation methods, and the co-
rating-based similarity measurement is widely used in CF for predicting ratings of unfamiliar …
rating-based similarity measurement is widely used in CF for predicting ratings of unfamiliar …
TrustWalker a random walk model for combining trust-based and item-based recommendation
Collaborative filtering is the most popular approach to build recommender systems and has
been successfully employed in many applications. However, it cannot make …
been successfully employed in many applications. However, it cannot make …
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 …
Using a trust network to improve top-n recommendation
Top-N item recommendation is one of the important tasks of recommenders. Collaborative
filtering is the most popular approach to building recommender systems which can predict …
filtering is the most popular approach to building recommender systems which can predict …
Merging trust in collaborative filtering to alleviate data sparsity and cold start
Providing high quality recommendations is important for e-commerce systems to assist users
in making effective selection decisions from a plethora of choices. Collaborative filtering is a …
in making effective selection decisions from a plethora of choices. Collaborative filtering is a …
[HTML][HTML] An effective recommender system by unifying user and item trust information for B2B applications
Q Shambour, J Lu - Journal of Computer and System Sciences, 2015 - Elsevier
Abstract Although Collaborative Filtering (CF)-based recommender systems have received
great success in a variety of applications, they still under-perform and are unable to provide …
great success in a variety of applications, they still under-perform and are unable to provide …