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Explanation-based Ranking in Opinionated Recommender Systems
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| File | Size | Format | |
|---|---|---|---|
| Download | insight_publication.pdf | 1.76 MB | Adobe PDF |
| 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 systems; Ranking signal; Informed 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 | 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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