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dc.contributor.authorPandey, Gaurav
dc.contributor.authorKotkov, Denis
dc.contributor.authorSemenov, Alexander
dc.date.accessioned2018-11-06T14:14:45Z
dc.date.available2018-11-06T14:14:45Z
dc.date.issued2018
dc.identifier.citationPandey, G., Kotkov, D., & Semenov, A. (2018). Recommending Serendipitous Items using Transfer Learning. In <i>CIKM '18 : Proceedings of the 27th ACM International Conference on Information and Knowledge Management</i> (pp. 1771-1774). ACM Press. <a href="https://doi.org/10.1145/3269206.3269268" target="_blank">https://doi.org/10.1145/3269206.3269268</a>
dc.identifier.otherCONVID_28680636
dc.identifier.otherTUTKAID_79268
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/60135
dc.description.abstractMost recommender algorithms are designed to suggest relevant items, but suggesting these items does not always result in user satisfaction. Therefore, the efforts in recommender systems recently shifted towards serendipity, but generating serendipitous recommendations is difficult due to the lack of training data. To the best of our knowledge, there are many large datasets containing relevance scores (relevance oriented) and only one publicly available dataset containing a relatively small number of serendipity scores (serendipity oriented). This limits the learning capabilities of serendipity oriented algorithms. Therefore, in the absence of any known deep learning algorithms for recommending serendipitous items and the lack of large serendipity oriented datasets, we introduce SerRec our novel transfer learning method to recommend serendipitous items. SerRec uses transfer learning to firstly train a deep neural network for relevance scores using a large dataset and then tunes it for serendipity scores using a smaller dataset. Our method shows benefits of transfer learning for recommending serendipitous items as well as performance gains over the state-of-the-art serendipity oriented algorithmsfi
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherACM Press
dc.relation.ispartofCIKM '18 : Proceedings of the 27th ACM International Conference on Information and Knowledge Management
dc.rightsIn Copyright
dc.subject.otherrecommender system
dc.subject.otherserendipity
dc.subject.otherdeep learning
dc.subject.othertransfer learning
dc.titleRecommending Serendipitous Items using Transfer Learning
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201810254529
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietojenkäsittelytiedefi
dc.contributor.oppiaineTietojärjestelmätiedefi
dc.contributor.oppiaineComputer Scienceen
dc.contributor.oppiaineInformation Systems Scienceen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2018-10-25T09:15:22Z
dc.relation.isbn978-1-4503-6014-2
dc.description.reviewstatuspeerReviewed
dc.format.pagerange1771-1774
dc.relation.issn2155-0751
dc.type.versionacceptedVersion
dc.rights.copyright© 2018 ACM
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceACM International Conference on Information and Knowledge Management
dc.subject.ysosuosittelujärjestelmät
dc.subject.ysokoneoppiminen
dc.subject.ysoalgoritmit
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p28483
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p14524
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1145/3269206.3269268


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