标题:Collaborative Filtering with Aspect-based Opinion Mining: A Tensor Factorization Approach
作者:Wang, Yuanhong; Liu, Yang; Yu, Xiaohui
通讯作者:Wang, Y
作者机构:[Wang, Yuanhong; Liu, Yang; Yu, Xiaohui] Shandong Univ, Sch Comp Sci & Technol, Jinan 250100, Shandong, Peoples R China.
会议名称:12th IEEE International Conference on Data Mining (ICDM)
会议日期:DEC 10-13, 2012
来源:12TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2012)
出版年:2012
页码:1152-1157
DOI:10.1109/ICDM.2012.76
关键词:Opinion Mining; Sentiment Analysis; Collaborative Filtering;; Recommendation System; Tensor Factorization
摘要:Collaborative filtering (CF) aims to produce user specific recommendations based on other users' ratings of items. Most existing CF methods rely only on users' overall ratings of items, ignoring the variety of opinions users may have towards different aspects of the items. Using the movie domain as a case study, we propose a framework that is able to capture users' opinions on different aspects from the textual reviews, and use that information to improve the effectiveness of CF. This framework has two components, an opinion mining component and a rating inference component. The former extracts and summarizes the opinions on multiple aspects from the reviews, generating ratings on the various aspects. The latter component, on the other hand, infers the overall ratings of items based on the aspect ratings, which forms the basis for item recommendation. Our core contribution is in the proposal of a tensor factorization approach for the rating inference. Operating on the tensor composed of the overall and aspect ratings, this approach is able to capture the intrinsic relationships between users, items, and aspects, and provide accurate predictions on unknown ratings. Experiments on a movie dataset show that our proposal significantly improves the prediction accuracy compared with two baseline methods.
收录类别:CPCI-S;EI;SCOPUS
WOS核心被引频次:9
Scopus被引频次:15
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84874067633&doi=10.1109%2fICDM.2012.76&partnerID=40&md5=628af5575510926ff0e8729feca48d26
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