Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/56854
Title: Using community preference for overcoming sparsity and cold-start problems in collaborative filtering system offering soft ratings
Authors: Van Doan Nguyen
Songsak Sriboonchitta
Van Nam Huynh
Keywords: Business, Management and Accounting
Computer Science
Issue Date: 1-Nov-2017
Abstract: © 2017 Elsevier B.V. This paper introduces a new collaborative filtering recommender system that is capable of offering soft ratings as well as integrating with a social network containing all users. Offering soft ratings is known as a new methodology for modeling subjective, qualitative, and imperfect information about user preferences, as well as a more realistic and flexible means for users to express their preferences on products and services. Additionally, in the system, community preferences that are extracted from the social network are employed for overcoming sparsity and cold-start problems. In the experiment, the new system is tested using a data set culled from Flixster, a social network focused on movies. The experiment's results show that this system is more effective than the selected baseline in terms of recommendation accuracy.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85032000382&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/56854
ISSN: 15674223
Appears in Collections:CMUL: Journal Articles

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