Evaluating user reputation in collaborative web search
|Title:||Evaluating user reputation in collaborative web search||Authors:||McNally, Kevin
O'Mahony, Michael P.
|Permanent link:||http://hdl.handle.net/10197/3478||Date:||23-Oct-2011||Abstract:||Often today’s recommender systems look to past user activity in order to influence future recommendations. In the case of social web search, employing collaborative recommendation techniques allows for personalization of search results. If recommendations arise from past user activity, the expertise of those users driving the recommendation process can play an important role when it comes to ensuring recommendation quality. Hence the reputation of users is important in collaborative and social search tasks, in addition to result relevance as traditionally considered in web search. In this paper we explore this concept of reputation; specifically, investigating how reputation can enhance the recommendation engine at the core of the HeyStaks social search utility. We evaluate a number of different reputation models in the context of the HeyStaks system, and demonstrate how incorporating reputation into the recommendation process can enhance the relevance of results recommended by HeyStaks.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Keywords:||Recommender systems; Search; HeyStaks||Subject LCSH:||Recommender systems (Information filtering)
Online social networks
|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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