We propose a recommendation method in which a user can find new interests that are partially similar to the user's taste. Partial similarity is an aspect of the ...
A Community-Based Recommendation System to Reveal Unexpected Interests ... system based on static community detection and item-based collaborative filtering ...
This approach is based on the clustering model and one of the mixed approaches of content-based and collaborative filtering. Community on the network is the ...
We propose a recommendation method in which a user can find new interests that are partially similar to the user's taste. Partial similarity is an aspect of the ...
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A Community-Based Recommendation System to Reveal Unexpected Interests. J. Kamahara, T. Asakawa, S. Shimojo, and H. Miyahara. Multimedia Modelling Conference ...
A community-based recommendation system to reveal unexpected interests ; 巻: pp.433-438 ; 号 ; 開始ページ: 433 ; 終了ページ: 438 ; 記述言語: 英語 ...
This paper introduces community-wise social interactions as a new dimension for recommendations and presents a social recommendation system using ...
Using static community detection algorithms, Kamahara et al. have proposed a community-based approach for recommender systems which can reveal unexpected user‟s.
Our novel recommendation method suggests communities that the target user has not seen but is potentially interested in, in order to broaden the user's horizon.
Recommender systems are filters which suggest items or information that might be interesting to users. These systems analyze the past behavior of a user, ...