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

Jing Yuan; Felix Lorenz; Andreas Lommatzsch; Mu Mu; Nicholas Race; Frank Hopfgartner; Sahin Albayrak

doi:10.1145/3077548.3077552

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

Jing Yuan, Felix Lorenz, Andreas Lommatzsch, Mu Mu, Nicholas Race, Frank Hopfgartner, Sahin Albayrak

Research output: Contribution to Book/Report › Conference Contribution › peer-review

Abstract

Context-awareness has become a critical factor to improve the prediction of user interest in modern online TV recommendation systems. In addition to individual user preferences, existing context-aware approaches like tensor factorization incorporate system-level contextual bias to increase predicting accuracy. We analyzed a user interaction dataset from a WebTV platform and found that such contextual bias creates a skewed selection of recommended programs which ultimately limits users in a filter bubble. To address this issue, we introduce the Twitter social stream as an external contextual factor 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.

Original language	English
Title of host publication	ACM International Conference on Interactive Experiences for Television and Online Video (ACM TVX)
Place of Publication	The Netherlands
Publisher	Association for Computing Machinery (ACM)
Pages	21-30
Number of pages	10
ISBN (Print)	978-1-4503-4529-3
DOIs	https://doi.org/10.1145/3077548.3077552
Publication status	Published - 16 Jun 2017

Keywords

Privacy reserving recommender
context-aware applications
event detection
userexperience
video on demand

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10.1145/3077548.3077552

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Yuan, J., Lorenz, F., Lommatzsch, A., Mu, M., Race, N., Hopfgartner, F., & Albayrak, S. (2017). Countering contextual bias in TV watching behavior: introducing social trend as external contextual factor in TV recommenders. In ACM International Conference on Interactive Experiences for Television and Online Video (ACM TVX) (pp. 21-30 ). Association for Computing Machinery (ACM). https://doi.org/10.1145/3077548.3077552

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title = "Countering contextual bias in TV watching behavior: introducing social trend as external contextual factor in TV recommenders",

abstract = "Context-awareness has become a critical factor to improve the prediction of user interest in modern online TV recommendation systems. In addition to individual user preferences, existing context-aware approaches like tensor factorization incorporate system-level contextual bias to increase predicting accuracy. We analyzed a user interaction dataset from a WebTV platform and found that such contextual bias creates a skewed selection of recommended programs which ultimately limits users in a filter bubble. To address this issue, we introduce the Twitter social stream as an external contextual factor 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.",

keywords = "Privacy reserving recommender, context-aware applications, event detection, userexperience, video on demand",

author = "Jing Yuan and Felix Lorenz and Andreas Lommatzsch and Mu Mu and Nicholas Race and Frank Hopfgartner and Sahin Albayrak",

year = "2017",

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Yuan, J, Lorenz, F, Lommatzsch, A, Mu, M, Race, N, Hopfgartner, F & Albayrak, S 2017, Countering contextual bias in TV watching behavior: introducing social trend as external contextual factor in TV recommenders. in ACM International Conference on Interactive Experiences for Television and Online Video (ACM TVX). Association for Computing Machinery (ACM), The Netherlands, pp. 21-30 . https://doi.org/10.1145/3077548.3077552

Countering contextual bias in TV watching behavior: introducing social trend as external contextual factor in TV recommenders. / Yuan, Jing; Lorenz, Felix; Lommatzsch, Andreas et al.
ACM International Conference on Interactive Experiences for Television and Online Video (ACM TVX). The Netherlands: Association for Computing Machinery (ACM), 2017. p. 21-30 .

Research output: Contribution to Book/Report › Conference Contribution › peer-review

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T1 - Countering contextual bias in TV watching behavior: introducing social trend as external contextual factor in TV recommenders

AU - Yuan, Jing

AU - Lorenz, Felix

AU - Lommatzsch, Andreas

AU - Mu, Mu

AU - Race, Nicholas

AU - Hopfgartner, Frank

AU - Albayrak, Sahin

PY - 2017/6/16

Y1 - 2017/6/16

N2 - Context-awareness has become a critical factor to improve the prediction of user interest in modern online TV recommendation systems. In addition to individual user preferences, existing context-aware approaches like tensor factorization incorporate system-level contextual bias to increase predicting accuracy. We analyzed a user interaction dataset from a WebTV platform and found that such contextual bias creates a skewed selection of recommended programs which ultimately limits users in a filter bubble. To address this issue, we introduce the Twitter social stream as an external contextual factor 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.

AB - Context-awareness has become a critical factor to improve the prediction of user interest in modern online TV recommendation systems. In addition to individual user preferences, existing context-aware approaches like tensor factorization incorporate system-level contextual bias to increase predicting accuracy. We analyzed a user interaction dataset from a WebTV platform and found that such contextual bias creates a skewed selection of recommended programs which ultimately limits users in a filter bubble. To address this issue, we introduce the Twitter social stream as an external contextual factor 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.

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Yuan J, Lorenz F, Lommatzsch A, Mu M, Race N, Hopfgartner F et al. Countering contextual bias in TV watching behavior: introducing social trend as external contextual factor in TV recommenders. In ACM International Conference on Interactive Experiences for Television and Online Video (ACM TVX). The Netherlands: Association for Computing Machinery (ACM). 2017. p. 21-30 doi: 10.1145/3077548.3077552

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

Abstract

Keywords

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