Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/12628
DC FieldValueLanguage
dc.contributor.authorChatzis, Sotirios P.-
dc.contributor.authorChristodoulou, Panayiotis-
dc.contributor.authorAndreou, Andreas S.-
dc.date.accessioned2018-08-08T09:47:32Z-
dc.date.available2018-08-08T09:47:32Z-
dc.date.issued2017-08-27-
dc.identifier.citationDLRS 2017 Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems, 2017, Como, Italy, 27 August, pp. 38-45en_US
dc.identifier.isbn978-1-4503-5353-3-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/12628-
dc.descriptionACM-ICPSACM International Conference Proceeding Seriesen_US
dc.description.abstractIn this work, we attempt to ameliorate the impact of data sparsity in the context of session-based recommendation. Specifically, we seek to devise a machine learning mechanism capable of extracting subtle and complex underlying temporal dynamics in the observed session data, so as to inform the recommendation algorithm. To this end, we improve upon systems that utilize deep learning techniques with recurrently connected units; we do so by adopting concepts from the field of Bayesian statistics, namely variational inference. Our proposed approach consists in treating the network recurrent units as stochastic latent variables with a prior distribution imposed over them. On this basis, we proceed to infer corresponding posteriors; these can be used for prediction and recommendation generation, in a way that accounts for the uncertainty in the available sparse training data. To allow for our approach to easily scale to large real-world datasets, we perform inference under an approximate amortized variational inference (AVI) setup, whereby the learned posteriors are parameterized via (conventional) neural networks. We perform an extensive experimental evaluation of our approach using challenging benchmark datasets, and illustrate its superiority over existing state-of-the-art techniques.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relationDOSSIER-CLOUD - Devops-Based Software Engineering for the Clouden_US
dc.rights© Association for Computing Machinery.en_US
dc.subjectAmortized variational inferenceen_US
dc.subjectData sparsityen_US
dc.subjectLatent variable modelen_US
dc.subjectRecurrent neural networken_US
dc.subjectSession-based recommendationen_US
dc.titleRecurrent latent variable networks for session-based recommendationen_US
dc.typeConference Papersen_US
dc.collaborationCyprus University of Technologyen_US
dc.subject.categoryComputer and Information Sciencesen_US
dc.journalsOpen Accessen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.relation.conferenceWorkshop on Deep Learning for Recommender Systemsen_US
dc.identifier.doi10.1145/3125486.3125493en_US
cut.common.academicyear2016-2017en_US
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.cerifentitytypePublications-
item.openairetypeconferenceObject-
crisitem.project.funderEC Joint Research Centre-
crisitem.project.grantnoDOSSIER-Cloud-
crisitem.project.fundingProgramH2020-
crisitem.project.openAireinfo:eu-repo/grantAgreement/EC/H2020/692251-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-4956-4013-
crisitem.author.orcid0000-0001-7104-2097-
crisitem.author.parentorgFaculty of Engineering and Technology-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation
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