Recommendation uncertainty in implicit feedback Recommender Systems

Coscranto, Victor; Bridge, Derek G.

Recommendation uncertainty in implicit feedback Recommender Systems

dc.contributor.author	Coscranto, Victor
dc.contributor.author	Bridge, Derek G.
dc.contributor.editor	Longo, L.
dc.contributor.editor	O'Reilly, Ruairi
dc.contributor.funder	Science Foundation Ireland	en
dc.date.accessioned	2023-01-11T10:04:34Z
dc.date.available	2023-01-11T10:04:34Z
dc.date.issued	2022-12-08
dc.date.updated	2023-01-11T09:54:14Z
dc.description.abstract	A Recommender System’s recommendations will each carry a certain level of uncertainty. The quantification of this uncertainty can be useful in a variety of ways. Estimates of uncertainty might be used externally; for example, showing them to the user to increase user trust in the abilities of the system. They may also be used internally; for example, deciding the balance of ‘safe’ and less safe recommendations. In this work, we explore several methods for estimating uncertainty. The novelty comes from proposing methods that work in the implicit feedback setting. We use experiments on two datasets to compare a number of recommendation algorithms that are modified to perform uncertainty estimation. In our experiments, we show that some of these modified algorithms are less accurate than their unmodified counterparts, but others are actually more accurate. We also show which of these methods are best at enabling the recommender to be ‘aware’ of which of its recommendations are likely to be correct and which are likely to be wrong.	en
dc.description.status	Peer reviewed	en
dc.description.version	Accepted Version	en
dc.format.mimetype	application/pdf	en
dc.identifier.citation	Coscrato, V. and Bridge, D. (2023) ‘Recommendation uncertainty in implicit feedback recommender systems’, AICS2022, in L. Longo and R. O’Reilly (eds) Artificial Intelligence and Cognitive Science. Cham: Springer Nature Switzerland, pp. 279–291. https://doi.org/10.1007/978-3-031-26438-2_22.	en
dc.identifier.doi	10.1007/978-3-031-26438-2_22
dc.identifier.endpage	291
dc.identifier.isbn	978-3-031-26438-2
dc.identifier.isbn	978-3-031-26437-5
dc.identifier.issn	1865-0929
dc.identifier.startpage	279
dc.identifier.uri	https://hdl.handle.net/10468/14036
dc.language.iso	en	en
dc.publisher	Springer Cham	en
dc.relation.project	info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/
dc.rights	© 2023 The Author(s). Open Access. This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made	en
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	en
dc.subject	Recommender systems	en
dc.subject	Uncertainty	en
dc.subject	Neural networks	en
dc.subject	Artificial intelligence	en
dc.title	Recommendation uncertainty in implicit feedback Recommender Systems	en
dc.type	Conference item	en