Personalised Opinion-based Recommendation

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Title: Personalised Opinion-based Recommendation
Authors: Smyth, Barry
Dong, Ruihai
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Date: 2-Nov-2016
Abstract: E-commerce recommender systems seek out matches betweencustomers and items in order to help customers discover more relevantand satisfying products and to increase the conversion rate of browsers tobuyers. To do this, a recommender system must learn about the likes anddislikes of customers/users as well as the advantages and disadvantages(pros and cons) of products. Recently, the explosion of user-generatedcontent, especially customer reviews, and other forms of opinionated expression,has provided a new source of user and product insights. Theinterests of a user can be mined from the reviews that they write andthe pros and cons of products can be mined from the reviews writtenabout them. In this paper, we build on recent work in this area to generateuser and product proles from user-generated reviews. We furtherdescribe how this information can be used in various recommendationtasks to suggest high-quality and relevant items to users based on eitheran explicit query or their prole. We evaluate these ideas using alarge dataset of TripAdvisor reviews. The results show the benets ofcombining sentiment and similarity in both query-based and user-basedrecommendation scenarios, and also disclose the eect of the number ofreviews written by a user on recommendation performance.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: Springer
Journal: Lecture Notes in Computer Science
Volume: 9969
Start page: 93
End page: 107
Copyright (published version): 2016 Springer
Keywords: Recommender systemsOpinion miningSentiment analysisPersonalisation
DOI: 10.1007/978-3-319-47096-2_7
Language: en
Status of Item: Peer reviewed
Is part of: Goel, A., Diaz-Agudo, M.B. and Roth-Berghofer, T.R. (eds.) Proceedings of 24th International Conference, ICCBR 2016, Atlanta, Georgia, USA, 31 October - 02 November 2016
Conference Details: 24th International Conference, ICCBR 2016, Atlanta, Georgia, USA, 31 October - 02 November 2016
Appears in Collections:Computer Science Research Collection
Insight Research Collection

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