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 proceedingConference proceeding contribution

17 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 languageEnglish
Title of host publicationProceedings of the 7th ACM conference on Recommender systems, RecSys '13
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages285-294
Number of pages10
ISBN (Electronic)9781450324090
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event7th ACM Conference on Recommender Systems, RecSys 2013 - Hong Kong, China
Duration: 12 Oct 201316 Oct 2013

Conference

Conference7th ACM Conference on Recommender Systems, RecSys 2013
CountryChina
CityHong Kong
Period12/10/1316/10/13

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Keywords

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

Cite this

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 (pp. 285-294). New York: Association for Computing Machinery (ACM). https://doi.org/10.1145/2507157.2507204