Watch-it-next: A contextual TV recommendation system

Michal Aharon; Eshcar Hillel; Amit Kagian; Ronny Lempel; Hayim Makabee; Raz Nissim

doi:10.1007/978-3-s319-23461-8_12

Watch-it-next: A contextual TV recommendation system

Michal Aharon, Eshcar Hillel, Amit Kagian, Ronny Lempel, Hayim Makabee, Raz Nissim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

14 Scopus citations

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-theart recommendation methods, exploiting the current watching context, demonstrate a significant improvement in recommendation quality.

Original language	English
Title of host publication	Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015, Proceedings
Editors	Bianca Zadrozny, Francesco Bonchi, Bianca Zadrozny, Jaime Cardoso, Francesco Bonchi, Ricard Gavalda, Francesco Bonchi, Myra Spiliopoulou, Ricard Gavalda, Dino Pedreschi, Jaime Cardoso, Myra Spiliopoulou, Jaime Cardoso, Dino Pedreschi, Francesco Bonchi, Albert Bifet, Dino Pedreschi, Albert Bifet, Bianca Zadrozny, Myra Spiliopoulou, Dino Pedreschi, Myra Spiliopoulou, Michael May, Michael May, Albert Bifet, Ricard Gavalda, Albert Bifet, Michael May, Bianca Zadrozny, Michael May, Jaime Cardoso, Ricard Gavalda
Publisher	Springer Verlag
Pages	180-195
Number of pages	16
ISBN (Print)	9783319234601, 9783319234601, 9783319234601, 9783319234601
DOIs	https://doi.org/10.1007/978-3-s319-23461-8_12
State	Published - 1 Jan 2015
Externally published	Yes
Event	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2015 - Porto, Portugal Duration: 7 Sep 2015 → 11 Sep 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9286
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2015
Country/Territory	Portugal
City	Porto
Period	7/09/15 → 11/09/15

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-s319-23461-8_12

Cite this

Aharon, M., Hillel, E., Kagian, A., Lempel, R., Makabee, H., & Nissim, R. (2015). Watch-it-next: A contextual TV recommendation system. In B. Zadrozny, F. Bonchi, B. Zadrozny, J. Cardoso, F. Bonchi, R. Gavalda, F. Bonchi, M. Spiliopoulou, R. Gavalda, D. Pedreschi, J. Cardoso, M. Spiliopoulou, J. Cardoso, D. Pedreschi, F. Bonchi, A. Bifet, D. Pedreschi, A. Bifet, B. Zadrozny, M. Spiliopoulou, D. Pedreschi, M. Spiliopoulou, M. May, M. May, A. Bifet, R. Gavalda, A. Bifet, M. May, B. Zadrozny, M. May, J. Cardoso, ... R. Gavalda (Eds.), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015, Proceedings (pp. 180-195). Article A12 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9286). Springer Verlag. https://doi.org/10.1007/978-3-s319-23461-8_12

Aharon, Michal ; Hillel, Eshcar ; Kagian, Amit et al. / Watch-it-next : A contextual TV recommendation system. Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015, Proceedings. editor / Bianca Zadrozny ; Francesco Bonchi ; Bianca Zadrozny ; Jaime Cardoso ; Francesco Bonchi ; Ricard Gavalda ; Francesco Bonchi ; Myra Spiliopoulou ; Ricard Gavalda ; Dino Pedreschi ; Jaime Cardoso ; Myra Spiliopoulou ; Jaime Cardoso ; Dino Pedreschi ; Francesco Bonchi ; Albert Bifet ; Dino Pedreschi ; Albert Bifet ; Bianca Zadrozny ; Myra Spiliopoulou ; Dino Pedreschi ; Myra Spiliopoulou ; Michael May ; Michael May ; Albert Bifet ; Ricard Gavalda ; Albert Bifet ; Michael May ; Bianca Zadrozny ; Michael May ; Jaime Cardoso ; Ricard Gavalda. Springer Verlag, 2015. pp. 180-195 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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-theart recommendation methods, exploiting the current watching context, demonstrate a significant improvement in recommendation quality.",

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Aharon, M, Hillel, E, Kagian, A, Lempel, R, Makabee, H & Nissim, R 2015, Watch-it-next: A contextual TV recommendation system. in B Zadrozny, F Bonchi, B Zadrozny, J Cardoso, F Bonchi, R Gavalda, F Bonchi, M Spiliopoulou, R Gavalda, D Pedreschi, J Cardoso, M Spiliopoulou, J Cardoso, D Pedreschi, F Bonchi, A Bifet, D Pedreschi, A Bifet, B Zadrozny, M Spiliopoulou, D Pedreschi, M Spiliopoulou, M May, M May, A Bifet, R Gavalda, A Bifet, M May, B Zadrozny, M May, J Cardoso & R Gavalda (eds), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015, Proceedings., A12, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9286, Springer Verlag, pp. 180-195, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2015, Porto, Portugal, 7/09/15. https://doi.org/10.1007/978-3-s319-23461-8_12

Watch-it-next: A contextual TV recommendation system. / Aharon, Michal; Hillel, Eshcar; Kagian, Amit et al.
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015, Proceedings. ed. / Bianca Zadrozny; Francesco Bonchi; Bianca Zadrozny; Jaime Cardoso; Francesco Bonchi; Ricard Gavalda; Francesco Bonchi; Myra Spiliopoulou; Ricard Gavalda; Dino Pedreschi; Jaime Cardoso; Myra Spiliopoulou; Jaime Cardoso; Dino Pedreschi; Francesco Bonchi; Albert Bifet; Dino Pedreschi; Albert Bifet; Bianca Zadrozny; Myra Spiliopoulou; Dino Pedreschi; Myra Spiliopoulou; Michael May; Michael May; Albert Bifet; Ricard Gavalda; Albert Bifet; Michael May; Bianca Zadrozny; Michael May; Jaime Cardoso; Ricard Gavalda. Springer Verlag, 2015. p. 180-195 A12 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9286).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2015

AU - Aharon, Michal

AU - Hillel, Eshcar

AU - Kagian, Amit

AU - Lempel, Ronny

AU - Makabee, Hayim

AU - Nissim, Raz

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

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Aharon M, Hillel E, Kagian A, Lempel R, Makabee H, Nissim R. Watch-it-next: A contextual TV recommendation system. In Zadrozny B, Bonchi F, Zadrozny B, Cardoso J, Bonchi F, Gavalda R, Bonchi F, Spiliopoulou M, Gavalda R, Pedreschi D, Cardoso J, Spiliopoulou M, Cardoso J, Pedreschi D, Bonchi F, Bifet A, Pedreschi D, Bifet A, Zadrozny B, Spiliopoulou M, Pedreschi D, Spiliopoulou M, May M, May M, Bifet A, Gavalda R, Bifet A, May M, Zadrozny B, May M, Cardoso J, Gavalda R, editors, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015, Proceedings. Springer Verlag. 2015. p. 180-195. A12. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-s319-23461-8_12

Watch-it-next: A contextual TV recommendation system

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