Catch-up TV recommendations: show old favourites and find new ones

Mengxi Xu; Shlomo Berkovsky; Sebastien Ardon; Sipat Triukose; Anirban Mahanti; Irena Koprinska

doi:10.1145/2507157.2507204

Catch-up TV recommendations: show old favourites and find new ones

Mengxi Xu, Shlomo Berkovsky, Sebastien Ardon, Sipat Triukose, Anirban Mahanti, Irena Koprinska

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

22 Citations (Scopus)

Abstract

Web-based catch-up TV has revolutionised watching habits as it provides users the opportunity to watch programs at their preferred time and place, using a variety of devices. With the increasing offer of TV content, there is an emergent need for personalised recommendation solutions, which help users to select programs of interest. In this work, we study the watching patterns of users of an Australian nation-wide catch-up TV service provider and develop a suite of approaches for a catch-up recommendation scenario. We evaluate these approaches using a new large-scale dataset gathered by the Web-based catch-up portal deployed by the provider. The evaluation allows us to compare the performance of several recommenders that address the discovery of both TV programs already watched by users and new programs that users may find relevant.

Original language	English
Title of host publication	Proceedings of the 7th ACM conference on Recommender systems, RecSys '13
Place of Publication	New York
Publisher	Association for Computing Machinery (ACM)
Pages	285-294
Number of pages	10
ISBN (Electronic)	9781450324090
DOIs	https://doi.org/10.1145/2507157.2507204
Publication status	Published - 2013
Externally published	Yes
Event	7th ACM Conference on Recommender Systems, RecSys 2013 - Hong Kong, China Duration: 12 Oct 2013 → 16 Oct 2013

Conference

Conference	7th ACM Conference on Recommender Systems, RecSys 2013
Country/Territory	China
City	Hong Kong
Period	12/10/13 → 16/10/13

Keywords

Recommender Systems
Catch-up TV
Grouped Video Content Recommendations
Large-Scale Evaluation

Access to Document

10.1145/2507157.2507204

Cite this

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title = "Catch-up TV recommendations: show old favourites and find new ones",

abstract = "Web-based catch-up TV has revolutionised watching habits as it provides users the opportunity to watch programs at their preferred time and place, using a variety of devices. With the increasing offer of TV content, there is an emergent need for personalised recommendation solutions, which help users to select programs of interest. In this work, we study the watching patterns of users of an Australian nation-wide catch-up TV service provider and develop a suite of approaches for a catch-up recommendation scenario. We evaluate these approaches using a new large-scale dataset gathered by the Web-based catch-up portal deployed by the provider. The evaluation allows us to compare the performance of several recommenders that address the discovery of both TV programs already watched by users and new programs that users may find relevant.",

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author = "Mengxi Xu and Shlomo Berkovsky and Sebastien Ardon and Sipat Triukose and Anirban Mahanti and Irena Koprinska",

year = "2013",

doi = "10.1145/2507157.2507204",

language = "English",

pages = "285--294",

booktitle = "Proceedings of the 7th ACM conference on Recommender systems, RecSys '13",

publisher = "Association for Computing Machinery (ACM)",

address = "United States",

note = "7th ACM Conference on Recommender Systems, RecSys 2013 ; Conference date: 12-10-2013 Through 16-10-2013",

}

Xu, M, Berkovsky, S, Ardon, S, Triukose, S, Mahanti, A & Koprinska, I 2013, Catch-up TV recommendations: show old favourites and find new ones. in Proceedings of the 7th ACM conference on Recommender systems, RecSys '13. Association for Computing Machinery (ACM), New York, pp. 285-294, 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China, 12/10/13. https://doi.org/10.1145/2507157.2507204

Catch-up TV recommendations: show old favourites and find new ones. / Xu, Mengxi; Berkovsky, Shlomo; Ardon, Sebastien et al.
Proceedings of the 7th ACM conference on Recommender systems, RecSys '13. New York: Association for Computing Machinery (ACM), 2013. p. 285-294.

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

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T1 - Catch-up TV recommendations

T2 - 7th ACM Conference on Recommender Systems, RecSys 2013

AU - Xu, Mengxi

AU - Berkovsky, Shlomo

AU - Ardon, Sebastien

AU - Triukose, Sipat

AU - Mahanti, Anirban

AU - Koprinska, Irena

PY - 2013

Y1 - 2013

N2 - Web-based catch-up TV has revolutionised watching habits as it provides users the opportunity to watch programs at their preferred time and place, using a variety of devices. With the increasing offer of TV content, there is an emergent need for personalised recommendation solutions, which help users to select programs of interest. In this work, we study the watching patterns of users of an Australian nation-wide catch-up TV service provider and develop a suite of approaches for a catch-up recommendation scenario. We evaluate these approaches using a new large-scale dataset gathered by the Web-based catch-up portal deployed by the provider. The evaluation allows us to compare the performance of several recommenders that address the discovery of both TV programs already watched by users and new programs that users may find relevant.

AB - Web-based catch-up TV has revolutionised watching habits as it provides users the opportunity to watch programs at their preferred time and place, using a variety of devices. With the increasing offer of TV content, there is an emergent need for personalised recommendation solutions, which help users to select programs of interest. In this work, we study the watching patterns of users of an Australian nation-wide catch-up TV service provider and develop a suite of approaches for a catch-up recommendation scenario. We evaluate these approaches using a new large-scale dataset gathered by the Web-based catch-up portal deployed by the provider. The evaluation allows us to compare the performance of several recommenders that address the discovery of both TV programs already watched by users and new programs that users may find relevant.

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KW - Grouped Video Content Recommendations

KW - Large-Scale Evaluation

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M3 - Conference proceeding contribution

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BT - Proceedings of the 7th ACM conference on Recommender systems, RecSys '13

PB - Association for Computing Machinery (ACM)

CY - New York

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ER -

Catch-up TV recommendations: show old favourites and find new ones

Abstract

Conference

Keywords

Access to Document

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Cite this