A multi-criteria evaluation of a user generated content based recommender system

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Title: A multi-criteria evaluation of a user generated content based recommender system
Authors: Garcia Esparza, SandraO'Mahony, Michael P.Smyth, Barry
Permanent link: http://hdl.handle.net/10197/3509
Date: 23-Oct-2011
Online since: 2012-02-09T16:50:24Z
Abstract: The Social Web provides new and exciting sources of information that may be used by recommender systems as a complementary source of recommendation knowledge. For example, User-Generated Content, such as reviews, tags, comments, tweets etc. can provide a useful source of item information and user preference data, if a clear signal can be extracted from the inevitable noise that exists within these sources. In previous work we explored this idea, mining term-based recommendation knowledge from user reviews, to develop a recommender that compares favourably to conventional collaborative-filtering style techniques across a range of product types. However, this previous work focused solely on recommendation accuracy and it is now well accepted in the literature that accuracy alone tells just part of the recommendation story. For example, for many, the promise of recommender systems lies in their ability to surprise with novel recommendations for less popular items that users might otherwise miss. This makes for a riskier recommendation prospect, of course, but it could greatly enhance the practical value of recommender systems to end-users. In this paper we analyse our User-Generated Content (UGC) approach to recommendation using metrics such as novelty, diversity, and coverage and demonstrate superior performance, when compared to conventional user-based and item- based collaborative filtering techniques, while highlighting a number of interesting performance trade-offs.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Keywords: Recommender systemsUser-generated contentPerformance metrics
Subject LCSH: Recommender systems (Information filtering)--Evaluation
User-generated content
Other versions: http://www.dcs.warwick.ac.uk/~ssanand/RSWeb11/rsweb2011proceedingsfinal.pdf
Language: en
Status of Item: Peer reviewed
Is part of: Freyne, J. et al. (eds.). Proceedings of the 3rd ACM RecSys’10 Workshop on Recommender Systems and the Social Web
Conference Details: Presented at the 3rd Workshop on Recommender Systems and the Social Web (RSWEB-11), 5th ACM Conference on Recommender Systems, Chicago, IL, USA, 23-27 October 2011
Appears in Collections:CLARITY Research Collection
Computer Science Research Collection

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