Watch-It-Next: A Contextual TV Recommendation System
Abstract
As consumers of television are presented with a plethora of available programming, improving recommender systems in this domain is becoming increasingly important. Television sets, though, are often shared by multiple users whose tastes may greatly vary. Recommendation systems are challenged by this setting, since viewing data is typically collected and modeled per device , aggregating over its users and obscuring their individual tastes. This paper tackles the challenge of TV recommendation, specifically aiming to provide recommendations for the next program to watch following the currently watched program the device. We present an empirical evaluation of several recommendation methods over large-scale, real-life TV viewership data. Our extentions of common state-of-the-art recommendation methods, exploiting the current watching context, demonstrate a significant improvement in recommendation quality.
Cite
Text
Aharon et al. "Watch-It-Next: A Contextual TV Recommendation System." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2015. doi:10.1007/978-3-319-23461-8_12Markdown
[Aharon et al. "Watch-It-Next: A Contextual TV Recommendation System." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2015.](https://mlanthology.org/ecmlpkdd/2015/aharon2015ecmlpkdd-watchitnext/) doi:10.1007/978-3-319-23461-8_12BibTeX
@inproceedings{aharon2015ecmlpkdd-watchitnext,
title = {{Watch-It-Next: A Contextual TV Recommendation System}},
author = {Aharon, Michal and Hillel, Eshcar and Kagian, Amit and Lempel, Ronny and Makabee, Hayim and Nissim, Raz},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2015},
pages = {180-195},
doi = {10.1007/978-3-319-23461-8_12},
url = {https://mlanthology.org/ecmlpkdd/2015/aharon2015ecmlpkdd-watchitnext/}
}