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dc.contributor.authorTekin, C.en_US
dc.contributor.authorSchaar, Mihaela van deren_US
dc.date.accessioned2019-02-13T07:52:38Z
dc.date.available2019-02-13T07:52:38Z
dc.date.issued2015-04en_US
dc.identifier.issn1520-9210
dc.identifier.urihttp://hdl.handle.net/11693/49382
dc.description.abstractThe last decade has witnessed a tremendous growth in the volume as well as the diversity of multimedia content generated by a multitude of sources (news agencies, social media, etc.). Faced with a variety of content choices, consumers are exhibiting diverse preferences for content; their preferences often depend on the context in which they consume content as well as various exogenous events. To satisfy the consumers’ demand for such diverse content, multimedia content aggregators (CAs) have emerged which gather content from numerous multimedia sources. A key challenge for such systems is to accurately predict what type of content each of its consumers prefers in a certain context, and adapt these predictions to the evolving consumers’ preferences, contexts, and content characteristics. We propose a novel, distributed, online multimedia content aggregation framework, which gathers content generated by multiple heterogeneous producers to fulfill its consumers’ demand for content. Since both the multimedia content characteristics and the consumers’ preferences and contexts are unknown, the optimal content aggregation strategy is unknown a priori. Our proposed content aggregation algorithm is able to learn online what content to gather and how to match content and users by exploiting similarities between consumer types. We prove bounds for our proposed learning algorithms that guarantee both the accuracy of the predictions as well as the learning speed. Importantly, our algorithms operate efficiently even when feedback from consumers is missing or content and preferences evolve over time. Illustrative results highlight the merits of the proposed content aggregation system in a variety of settings.en_US
dc.language.isoEnglishen_US
dc.source.titleIEEE Transactions on Multimediaen_US
dc.relation.isversionofhttps://doi.org/10.1109/TMM.2015.2403234en_US
dc.subjectContent aggregationen_US
dc.subjectDistributed online learningen_US
dc.subjectMulti-armed banditsen_US
dc.subjectSocial multimediaen_US
dc.titleContextual online learning for multimedia content aggregationen_US
dc.typeArticleen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.citation.spage549en_US
dc.citation.epage561en_US
dc.citation.volumeNumber17en_US
dc.citation.issueNumber4en_US
dc.identifier.doi10.1109/TMM.2015.2403234en_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.identifier.eissn1941-0077


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