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In this work we present a varation of kNN, the trusted k-nearest recommenders (or kNR) algorithm, which allows users to learn who and how much to trust one ...
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We explore the use of regular equivalence applied to a trust network to generate a similarity matrix that is used to select the k-nearest neighbors for ...
We develop a novel computation model of trust by incorporating the tastes of users. Then we propagate trust throughout the trust relationship network, and more ...
Oct 22, 2024 · Collaborative filtering is a widely accepted technique to generate recommendations based on the ratings of like-minded users. However, it ...
Jun 17, 2024 · This paper explores recommender systems in social networks which leverage information such as item rating, intra-item similarities, and trust graph.
In this paper, we have developed a trust-based collaborative filtering recommendation system using the blockchain technology. In this system, a blockchain holds ...
In this work we present a varation of. kNN, the trusted k-nearest recommenders (or kNR) algorithm, which allows users to learn who and how much to trust one ...
Firstly, we analyze users' behaviors and relationships in social network, and propose a trust calculation method based on Dijkstra's algorithm. Secondly, we ...
The values of trust among users are adjusted by using the reinforcement learning algorithm. On the basis of this, a user trust-based collaborative filtering ...
Therefore, we propose a hybrid trust model which integrates personal and group trust in order to improve the performance of collaborative filtering. From the ...