Mining the Real-Time Web: A Novel Approach to Product Recommendation
|Title:||Mining the Real-Time Web: A Novel Approach to Product Recommendation||Authors:||Garcia Esparza, Sandra
O'Mahony, Michael P.
|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 content; Micro-blogging; Recommender systems; Information retrieval; Real-time web||Subject LCSH:||User-generated content
Recommender systems (Information filtering)
|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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