Countering contextual bias in TV watching behavior:introducing social trend as external contextual factor in TV recommenders

Lorenz, Felix and Yuan, Jing and Lommatzsch, Andreas and Mu, Mu and Race, Nicholas and Hopfgartner, Frank and Albayrak, Sahin (2017) Countering contextual bias in TV watching behavior:introducing social trend as external contextual factor in TV recommenders. In: Proceedings of the 2017 ACM International Conference on Interactive Experiences for TV and Online Video. TVX '17 . ACM, New York, NY, USA, pp. 21-30. ISBN 9781450345293

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Abstract

Context-awareness has become a critical factor in improving the predictions of user interest in modern online TV recommendation systems. In addition to individual user preferences, existing context-aware approaches such as tensor factorization incorporate system-level contextual bias to increase predicting accuracy. We analyzed a user interaction dataset from a WebTV platform, and identified that such contextual bias creates a skewed selection of recommended programs which ultimately locks users in a filter bubble. To address this issue, we introduce the Twitter social stream as a source of external context to extend the choice with items related to social media events. We apply two trend indicators, Trend Momentum and SigniScore, to the Twitter histories of relevant programs. The evaluation reveals that Trend Momentum outperforms SigniScore and signalizes 96% of all peaks ahead of time regarding the selected candidate program titles.

Item Type:
Contribution in Book/Report/Proceedings
Subjects:
ID Code:
86859
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Deposited On:
26 Jun 2017 09:14
Refereed?:
Yes
Published?:
Published
Last Modified:
28 Oct 2020 08:50