Explanation-based Ranking in Opinionated Recommender Systems

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Title: Explanation-based Ranking in Opinionated Recommender Systems
Authors: Muhammad, Khalil
Lawlor, Aonghus
Smyth, Barry
Permanent link: http://hdl.handle.net/10197/9946
Date: 21-Sep-2018
Online since: 2019-04-15T10:05:08Z
Abstract: Explanations can help people to make better choices, but their use in recommender systems has so far been limited to the annotation of recommendations after they have been ranked and suggested to the user. In this paper we argue that explanations can also be used to rank recommendations. We describe a technique that uses the strength of an item’s explanation as a ranking signal – preferring items with compelling explanations – and demonstrate its efficacy on a real-world dataset.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: CEUR Workshop Proceedings
Keywords: Recommender systemsRanking signalInformed decision making
Other versions: http://aics2016.ucd.ie/
Language: en
Status of Item: Peer reviewed
Conference Details: The 24th Irish Conference on Artificial Intelligence and Cognitive Science, University College Dublin, Ireland, 20-21 September 2016
Appears in Collections:Insight Research Collection

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