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Toward Explainable Recommendations: Generating Review Text from Multicriteria Evaluation Data

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/72489

Title: Toward Explainable Recommendations: Generating Review Text from Multicriteria Evaluation Data
Authors: Suzuki, Takafumi Browse this author
Oyama, Satoshi Browse this author →KAKEN DB
Kurihara, Masahito Browse this author →KAKEN DB
Keywords: Decoding
Data models
Recommender systems
Mathematical model
Computational modeling
History
Recurrent neural networks
explainable recommendation
text generation
RNN
recommender systems
Issue Date: 2018
Publisher: IEEE (Institute of Electrical and Electronics Engineers)
Citation: 2018 IEEE International Conference on Big Data (Big Data), ISBN: 978-1-5386-5035-6
Start Page: 3549
End Page: 3551
Publisher DOI: 10.1109/BigData.2018.8622439
Abstract: Explaining recommendations helps users to make more accurate and effective decisions and improves system credibility and transparency. Current explainable recommender systems tend to provide fixed statements such as ”customers who purchased this item also purchased....”. This explanation is generated only on the basis of the purchase history of similar customers, so it does not include the preferences of customers who have purchased the item or a description of the item. Since user-generated reviews generally contain information about the reviewer’s preferences and a description of the item, such reviews typically have more effect on purchase decisions. Therefore, using reviews to explain recommendations should be more useful than providing only a fixed statement explanation. Aiming to create a system that provides personalized explanations for recommendations, we have developed a recurrent neural network model that uses multicriteria evaluation data to generate reviews.
Conference Name: IEEE International Conference on Big Data (Big Data)
Conference Sequence: 2018
Conference Place: Seattle, WA
Rights: © 2018 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/2115/72489
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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