On the Use of Opinionated Explanations to Rank and Justify Recommendations
|Title:||On the Use of Opinionated Explanations to Rank and Justify Recommendations||Authors:||Muhammad, Khalil; Lawlor, Aonghus; Smyth, Barry||Permanent link:||http://hdl.handle.net/10197/9032||Date:||18-May-2016||Online since:||2017-11-06T18:18:44Z||Abstract:||Explanations are an important part of modern recommendersystems. They help users to make better decisions, improvethe conversion rate of browsers into buyers, and lead togreater user satisfaction in the long-run. In this paper, we extendrecent work on generating explanations by mining userreviews. We show how this leads to a novel explanation formatthat can be tailored for the needs of the individual user.Moreover, we demonstrate how the explanations themselvescan be used to rank recommendations so that items which canbe associated with a more compelling explanation are rankedahead of items that have a less compelling explanation. Weevaluate our approach using a large-scale, real-world TripAdvisordataset.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Publisher:||Association for the Advancement of Artificial Intelligence||Copyright (published version):||2016 Association for the Advancement of Artificial Intelligence||Keywords:||Recommender Systems; Explanations; Opinion mining; Sentiment analysis||Other versions:||http://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS16/paper/view/12921||Language:||en||Status of Item:||Peer reviewed||Conference Details:||FLAIRS 2016, the 29th International Florida Artificial Intelligence Research Society Conference, Key Largo, Florida||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Insight Research Collection|
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