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.
Original language | English |
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Title of host publication | UMAP 2018 - Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization : UMAP '18 |
Number of pages | 2 |
Place of Publication | Singapore, Singapore |
Publisher | Association for Computing Machinery |
Publication date | 3 Jul 2018 |
Pages | 367-368 |
ISBN (Print) | 978-1-4503-5589-6/18/07 |
ISBN (Electronic) | 9781450355896 |
DOIs | |
Publication status | Published - 3 Jul 2018 |
Event | the 26th Conference on User Modeling, Adaptation and Personalization: U`Map 18 - Singapore, Singapore Duration: 8 Jul 2018 → 11 Jun 2019 |
Conference
Conference | the 26th Conference on User Modeling, Adaptation and Personalization |
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Country/Territory | Singapore |
City | Singapore |
Period | 08/07/2018 → 11/06/2019 |
Keywords
- Context-Awareness
- Dataset
- Experience Sampling
- Machine Learning
- Recommender Systems
- Television
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Dive into the research topics of 'A Dataset for Inferring Contextual Preferences of Users Watching TV'. Together they form a unique fingerprint.Datasets
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Contextual TV dataset
Kristoffersen, M. S. (Creator), Shepstone, S. E. (Creator) & Tan, Z. (Creator), VBN, 23 Dec 2020
DOI: 10.5278/28080cdd-6549-4cd6-8b46-e4d48c577937
Dataset
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