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 …
How to boost the performance of recommender systems by social trust? Studying the challenges and proposing a solution
With the increasing number of items in electronic retailers, news websites, etc., finding
interesting items concerning the taste of users is becoming more challenging …
interesting items concerning the taste of users is becoming more challenging …
Stochastic trust network enriched by similarity relations to enhance trust-aware recommendations
M Ghavipour, MR Meybodi - Applied Intelligence, 2019 - Springer
Collaborative filtering (CF) is the most popular recommendation approach that has been
extensively employed in recommender systems. However, it suffers from some weaknesses …
extensively employed in recommender systems. However, it suffers from some weaknesses …
Multi-faceted trust-based collaborative filtering
Many collaborative recommender systems leverage social correlation theories to improve
suggestion performance. However, they focus on explicit relations between users and they …
suggestion performance. However, they focus on explicit relations between users and they …
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 …
Collaborative filtering recommendation based on trust and emotion
With the development of personalized recommendations, information overload has been
alleviated. However, the sparsity of the user-item rating matrix and the weak transitivity of …
alleviated. However, the sparsity of the user-item rating matrix and the weak transitivity of …
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 …
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 …
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 …
[HTML][HTML] AgreeRelTrust—a simple implicit trust inference model for memory-based collaborative filtering recommendation systems
A Zahir, Y Yuan, K Moniz - Electronics, 2019 - mdpi.com
Recommendation systems alleviate the problem of information overload by helping users
find information relevant to their preference. Memory-based recommender systems use …
find information relevant to their preference. Memory-based recommender systems use …