Improving recommendation by deep latent factor-based explanation

DC FieldValueLanguage
dc.contributor.authorOuyang, Sixun-
dc.contributor.authorLawlor, Aonghus-
dc.date.accessioned2020-11-10T16:54:03Z-
dc.date.available2020-11-10T16:54:03Z-
dc.date.issued2020-02-12-
dc.identifier.urihttp://hdl.handle.net/10197/11682-
dc.descriptionThe Thirty-Fourth AAAI Conference on Artificial Intelligence: Interactive and Conversational Recommendation Systems (WICRS) Workshop, New York, United States of America, 7-12 February 2020en_US
dc.description.abstractThe latent factor methods and explanation algorithms constitute the foundation of many advanced explainable recommender systems. However, interpreting the high-dimensional latent factors has not been sufficiently addressed and continuously becomes a challenging work. Besides, only a few works have researched the use of explanation to improve recommendations. In this paper, we propose a deep learning method that generates high-quality latent factor-based explanations and efficiently ameliorating recommendations. We conduct top- K items ranking experiment on two real-world datasets and show that our method outperforms nine currently state-of-theart recommender systems in five ranking metrics. Moreover, we conduct a qualitative and quantitative analysis of users’ latent factors and reveal that we continually offer the best latent representations.en_US
dc.description.sponsorshipScience Foundation Irelanden_US
dc.language.isoenen_US
dc.subjectRecommender Systemsen_US
dc.subjectLatent representationen_US
dc.titleImproving recommendation by deep latent factor-based explanationen_US
dc.typeConference Publicationen_US
dc.statusPeer revieweden_US
dc.neeo.contributorOuyang|Sixun|aut|-
dc.neeo.contributorLawlor|Aonghus|aut|-
dc.date.embargo2020-02-12en_US
dc.description.othersponsorshipInsight Research Centreen_US
dc.date.updated2020-02-05T13:32:50Z-
dc.rights.licensehttps://creativecommons.org/licenses/by-nc-nd/3.0/ie/en
item.fulltextWith Fulltext-
item.grantfulltextopen-
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