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Multi-Clustering in Fast Collaborative Filtering Recommender Systems

Authors:
Urszula Kużelewska

Keywords: Recommender systems; Multi-clustering; Collaborative filtering

Abstract:
Searching the huge amount of information available on the Internet is undoubtedly a challenging task. A lot of new Web sites are created every day, containing not only text, but other types of resources: e.g., songs, movies or images. As a consequence, a simple search result list from search engines becomes insufficient. Recommender systems are the solution supporting users in finding items, which are interesting for them. These items may be information as well as products, in general. The main distinctive feature of recommender systems is taking into account the personal needs and tastes of users. Collaborative filtering approach is based on users' interactions with the electronic system. Its main challenge is generating on-line recommendations in reasonable time when coping with a large data size. Appropriate tools to support recommender systems in increasing time efficiency are clustering algorithms, which find similarities in off-line mode. Commonly, this causes a decrease in prediction accuracy of the final recommendations. This article presents a high time efficiency approach based on multi-clustered data, which avoids negative consequences. The input data is represented by clusters of similar items or users, where one item or user may belong to more than one cluster. When recommendations are generated, the best cluster for the user or item is selected. The best cluster means that the user or item is the most similar to the center of the cluster. As a result, the final accuracy is not decreased.

Pages: 85 to 90

Copyright: Copyright (c) IARIA, 2018

Publication date: October 14, 2018

Published in: conference

ISSN: 2308-4235

ISBN: 978-1-61208-668-2

Location: Nice, France

Dates: from October 14, 2018 to October 18, 2018