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Electronic Commerce Research

Erschienen in:

08.10.2016

Finding users preferences from large-scale online reviews for personalized recommendation

verfasst von: Yue Ma, Guoqing Chen, Qiang Wei

Erschienen in: Electronic Commerce Research | Ausgabe 1/2017

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Abstract

Along with the growth of Internet and electronic commerce, online consumer reviews have become a prevalent and rich source of information for both consumers and merchants. Numerous reviews record massive consumers’ opinions on products or services, which offer valuable information about users’ preferences for various aspects of different entities. This paper proposes a novel approach to finding the user preferences from free-text online reviews, where a user-preference-based collaborative filtering approach, namely UPCF, is developed to discover important aspects to users, as well as to reflect users’ individual needs for different aspects for recommendation. Extensive experiments are conducted on the data from a real-world online review platform, with the results showing that the proposed approach outperforms other approaches in effectively predicting the overall ratings of entities to target users for personalized recommendations. It also demonstrates that the approach has an advantage in dealing with sparse data, and can provide the recommendation results with desirable understandability.

Vorheriger Artikel Introduction to the special issue of ECR on E-business innovation with big data

Nächster Artikel The determinants of online customer ratings: a combined domain ontology and topic text analytics approach

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Titel: Finding users preferences from large-scale online reviews for personalized recommendation
verfasst von: Yue Ma
Guoqing Chen
Qiang Wei
Publikationsdatum: 08.10.2016
Verlag: Springer US
Erschienen in: Electronic Commerce Research / Ausgabe 1/2017
Print ISSN: 1389-5753
Elektronische ISSN: 1572-9362
DOI: https://doi.org/10.1007/s10660-016-9240-9

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