A Dataset for Inferring Contextual Preferences of Users Watching TV

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Abstract

Studies have shown that contextual settings play an important role in users' decision processes of what to consume, but data supporting the investigation of context-aware recommender systems are scarce. In this paper we present a TV consumption dataset enriched with contextual information of viewing situations. The dataset is designed for studying the intrinsic complexity of TV watching activities, and hence we also evaluate the performance of predicting chosen genres given contextual settings, and compare the results to contextless predictions. The results suggest a significant improvement by including contextual features in the prediction.
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Studies have shown that contextual settings play an important role in users' decision processes of what to consume, but data supporting the investigation of context-aware recommender systems are scarce. In this paper we present a TV consumption dataset enriched with contextual information of viewing situations. The dataset is designed for studying the intrinsic complexity of TV watching activities, and hence we also evaluate the performance of predicting chosen genres given contextual settings, and compare the results to contextless predictions. The results suggest a significant improvement by including contextual features in the prediction.
Original languageEnglish
Title of host publicationUMAP 2018 - Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization : UMAP '18
Number of pages2
Place of PublicationSingapore, Singapore
PublisherAssociation for Computing Machinery
Publication date3 Jul 2018
Pages367-368
ISBN (Print)978-1-4503-5589-6/18/07
ISBN (Electronic)9781450355896
DOI
Publication statusPublished - 3 Jul 2018
Publication categoryResearch
Peer-reviewedYes
Event the 26th Conference on User Modeling, Adaptation and Personalization: U`Map 18 - Singapore, Singapore
Duration: 8 Jul 201811 Jun 2019

Conference

Conference the 26th Conference on User Modeling, Adaptation and Personalization
LandSingapore
BySingapore
Periode08/07/201811/06/2019

    Research areas

  • Context-Awareness, Dataset, Experience Sampling, Machine Learning, Recommender Systems, Television

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