A comparative study of collaboration-based reputation models for social recommender systems
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|Title:||A comparative study of collaboration-based reputation models for social recommender systems||Authors:||McNally, Kevin
O'Mahony, Michael P.
|Permanent link:||http://hdl.handle.net/10197/8440||Date:||Aug-2014||Abstract:||Today, people increasingly leverage their online social networks to discover meaningful and relevant information, products and services. Thus, the ability to identify reputable online contacts with whom to interact has become ever more important. In this work we describe a generic approach to modeling user and item reputation in social recommender systems. In particular, we show how the various interactions between producers and consumers of content can be used to create so-called collaboration graphs, from which the reputation of users and items can be derived. We analyze the performance of our reputation models in the context of the HeyStaks social search platform, which is designed to complement mainstream search engines by recommending relevant pages to users based on the past experiences of search communities. By incorporating reputation into the existing HeyStaks recommendation framework, we demonstrate that the relevance of HeyStaks recommendations can be significantly improved based on data recorded during a live-user trial of the system.||Funding Details:||Science Foundation Ireland||Type of material:||Journal Article||Publisher:||Springer||Journal:||User Modeling and User-Adapted Interaction||Volume:||24||Issue:||3||Start page:||219||End page:||260||Copyright (published version):||2013 Springer||Keywords:||Recommender systems; Reputation; Social recommender systems; Collaboration graphs||DOI:||10.1007/s11257-013-9143-6||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||CLARITY Research Collection|
Computer Science Research Collection
Insight Research Collection
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