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Partial Commitment – “Try Before You Buy” and “Buyer’s Remorse” for Personal Data in Big Data & Machine Learning Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Reputation-Enhanced Recommender Systems

Authors : Christian Richthammer, Michael Weber, Günther Pernul

Published in: Trust Management XI

Publisher: Springer International Publishing

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Abstract

Recommender systems are pivotal components of modern Internet platforms and constitute a well-established research field. By now, research has resulted in highly sophisticated recommender algorithms whose further optimization often yields only marginal improvements. This paper goes beyond the commonly dominating focus on optimizing algorithms and instead follows the idea of enhancing recommender systems with reputation data. Since the concept of reputation-enhanced recommender systems has attracted considerable attention in recent years, the main aim of the paper is to provide a comprehensive survey of the approaches proposed so far. To this end, existing work are identified by means of a systematic literature review and classified according to carefully considered dimensions. In addition, the resulting structured analysis of the state of the art serves as a basis for the deduction of future research directions.

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Footnotes
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Metadata
Title
Reputation-Enhanced Recommender Systems
Authors
Christian Richthammer
Michael Weber
Günther Pernul
Copyright Year
2017
Book
Trust Management XI

Print ISBN: 978-3-319-59170-4

Electronic ISBN: 978-3-319-59171-1

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-59171-1_13

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