Recommendation based on co-similarity and spanning tree with minimum weight
Recommender system is a system that helps users find interesting items. Actually, collaborative filtering technology is one of the most successful techniques in recommender system. In this article we propose a new approach based on the rating of the users that is similar to the active one. In the li...
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Published in | Second International Conference on the Innovative Computing Technology (INTECH 2012) pp. 355 - 359 |
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Main Authors | , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.09.2012
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Subjects | |
Online Access | Get full text |
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Summary: | Recommender system is a system that helps users find interesting items. Actually, collaborative filtering technology is one of the most successful techniques in recommender system. In this article we propose a new approach based on the rating of the users that is similar to the active one. In the literature, we find a lot of approaches able to recommend items to the user. Aiming to offer a list of interesting items, we use a hybrid approach of collaborative filtering that performs better than others. Our collaborative filtering approach is based on the graph theory, so we use the dissimilarity matrix as a spanning tree with minimum weight based on Kruskal algorithm. We define a group of criteria that help to determine the best items to recommend without computing the rating prediction. |
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ISBN: | 146732678X 9781467326780 |
DOI: | 10.1109/INTECH.2012.6457807 |