Title: Personal classification space-based collaborative filtering algorithms

Authors: Takahisa Shirakawa; Setsuya Kurahashi

Addresses: NEC Personal Products, Ltd., 11-1, Osaki 1-chome, Shinagawa-ku, Tokyo 141-0032, Japan ' Graduate School of Business Sciences, University of Tsukuba, 3-29-1 Otsuka, Bunkyo, Tokyo 112-0012, Japan

Abstract: This research proposes a new similar information recommendation system focusing on social bookmarking, which organises bookmarks by using tags. Since social bookmarking targets a wide variety of genres of web pages, this research handles the problem where the standard collaborative filtering method cannot offer recommendations with a better level of precision. This paper improves the collaborative filtering algorithm for users of social bookmark services. A user's bookmarks are placed on his/her own classifying space made of tags. These bookmarks are transformed into a degree of similarity for recommendations. The degree is used to compare the personal classifying space with another's space. Comparison with previous studies confirms the superiority of the method based on space classification, in particular, where the cos distance with the distribution weight added is used as similarity between items. This proposed method shows a significant superiority.

Keywords: social bookmarks; recommendation systems; recommender systems; collaborative filtering; multi-genre problem; personal classification space; social bookmarking; tags; degree of similarity.

DOI: 10.1504/IJCAT.2013.051383

International Journal of Computer Applications in Technology, 2013 Vol.46 No.1, pp.3 - 12

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 31 Dec 2012 *

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