A comparative study of collaboration-based reputation models for social recommender systems

Title: A comparative study of collaboration-based reputation models for social recommender systems
Authors: McNally, KevinO'Mahony, Michael P.Smyth, Barry
Permanent link: http://hdl.handle.net/10197/8440
Date: Aug-2014
Online since: 2017-04-19T14:59:40Z
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 systemsReputationSocial recommender systemsCollaboration graphs
DOI: 10.1007/s11257-013-9143-6
Language: en
Status of Item: Peer reviewed
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:CLARITY Research Collection
Computer Science Research Collection
Insight Research Collection

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