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 leaves users in a filter bubble. To address this issue, we introduce a 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.

@inproceedings{LorenzetAl-TVX,
author = {Felix Lorenz and Jing Yuan, Andreas Lommatzsch and Mu Mu and Nicholas Race and Frank Hopfgartner and Sahin Albayrak},
title = {Countering Contextual Bias in TV Watching Behavior: Introducing Social Trend as External Contextual Factor in TV Recommenders},
booktitle = {Proc. of ACM TVX 2017},
year = {2017},
isbn = {978-1-4503-4529-3},
numpages = {10},
location = {Hilversum, The Netherlands},
doi = {10.1145/3077548.3077552},
publisher = {ACM},
address = {},
}