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Generating virtual ratings from chinese reviews to augment online recommendations

Published: 01 February 2013 Publication History

Abstract

Collaborative filtering (CF) recommenders based on User-Item rating matrix as explicitly obtained from end users have recently appeared promising in recommender systems. However, User-Item rating matrix is not always available or very sparse in some web applications, which has critical impact to the application of CF recommenders. In this article we aim to enhance the online recommender system by fusing virtual ratings as derived from user reviews. Specifically, taking into account of Chinese reviews' characteristics, we propose to fuse the self-supervised emotion-integrated sentiment classification results into CF recommenders, by which the User-Item Rating Matrix can be inferred by decomposing item reviews that users gave to the items. The main advantage of this approach is that it can extend CF recommenders to some web applications without user rating information. In the experiments, we have first identified the self-supervised sentiment classification's higher precision and recall by comparing it with traditional classification methods. Furthermore, the classification results, as behaving as virtual ratings, were incorporated into both user-based and item-based CF algorithms. We have also conducted an experiment to evaluate the proximity between the virtual and real ratings and clarified the effectiveness of the virtual ratings. The experimental results demonstrated the significant impact of virtual ratings on increasing system's recommendation accuracy in different data conditions (i.e., conditions with real ratings and without).

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        cover image ACM Transactions on Intelligent Systems and Technology
        ACM Transactions on Intelligent Systems and Technology  Volume 4, Issue 1
        Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
        January 2013
        357 pages
        ISSN:2157-6904
        EISSN:2157-6912
        DOI:10.1145/2414425
        Issue’s Table of Contents
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        Publication History

        Published: 01 February 2013
        Accepted: 01 August 2011
        Revised: 01 January 2011
        Received: 01 August 2010
        Published in TIST Volume 4, Issue 1

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        Author Tags

        1. Information retrieval
        2. online recommendation
        3. sentiment analysis

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        • (2023)Metadata based Cross-Domain Recommender Framework using Neighborhood Mapping2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)10.1109/CSDE59766.2023.10487686(1-8)Online publication date: 4-Dec-2023
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