Mining the Real-Time Web: A Novel Approach to Product Recommendation

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Title: Mining the Real-Time Web: A Novel Approach to Product Recommendation
Authors: Garcia Esparza, Sandra
O'Mahony, Michael P.
Smyth, Barry
Permanent link: http://hdl.handle.net/10197/3746
Date: May-2012
Abstract: Real-time web (RTW) services such as Twitter allow users to express their opinions and interests, often expressed in the form of short text messages providing abbreviated and highly personalized commentary in real-time. Although this RTW data is far from the structured data (movie ratings, product features, etc.) that is familiar to recommender systems research, it can contain useful consumer reviews on products, services and brands. This paper describes how Twitter-like short-form messages can be leveraged as a source of indexing and retrieval information for product recommendation. In particular, we describe how users and products can be represented from the terms used in their associated reviews. An evaluation performed on four different product datasets from the Blippr service shows the potential of this type of recommendation knowledge, and the experiments show that our proposed approach outperforms a more traditional collaborative-filtering based approach.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: Elsevier
Journal: Knowledge-Based Systems
Volume: 29
Issue: May 2012
Start page: 3
End page: 11
Copyright (published version): 2012 Elsevier B.V
Keywords: User-generated contentMicro-bloggingRecommender systemsInformation retrievalReal-time web
Subject LCSH: User-generated content
Microblogs
Recommender systems (Information filtering)
Web 2.0
DOI: 10.1016/j.knosys.2011.07.007
Language: en
Status of Item: Peer reviewed
Appears in Collections:CLARITY Research Collection
Computer Science Research Collection

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