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Collaborative filtering and rating aggregation based on multicriteria rating

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/68174

Title: Collaborative filtering and rating aggregation based on multicriteria rating
Authors: Morise, Hiroki Browse this author
Oyama, Satoshi Browse this author →KAKEN DB
Kurihara, Masahito Browse this author →KAKEN DB
Keywords: Recommendation
multicriteria rating
collaborative filtering
rating aggregation
Issue Date: 2017
Publisher: IEEE
Citation: 2017 IEEE International Conference on Big Data (BIGDATA), ISBN: 978-1-5386-2714-3
Start Page: 4417
End Page: 4422
Publisher DOI: 10.1109/BigData.2017.8258477
Abstract: Ratings by users on various items such as hotels and movies have become easily available on the Web. In many cases, other than overall rating for each item by each user, more detailed information such as ratings from different viewpoints and free text comments, as well as aggregated information such as the average of ratings by different users, are also available. We investigated the effectiveness of six existing collaborative filtering methods for large-scale sparse multicriteria rating data. We formulated rating aggregation as a collaborative filtering problem and applied six collaborative filtering methods to it. Furthermore, we extended three of the methods to calculate user similarity using indirect users and review comments and applied them to collaborative filtering and rating aggregation. The results show that multicriteria rating approaches perform better than single criterion rating approaches. The extended methods had better performance both in collaborative filtering and in rating aggregation.
Description: 2017 IEEE International Conference on Big Data(Big Data 2017) . December 11-14, 2017, Boston, MA, USA
Conference Name: IEEE International Conference on Big Data (BIGDATA)
Conference Sequence: 2017
Conference Place: Boston, MA
Rights: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Type: proceedings (author version)
URI: http://hdl.handle.net/2115/68174
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 小山 聡

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