A spatio-temporal recommender system for on-demand cinemas

Taofeng Xue, Beihong Jin, Beibei Li, Weiqing Wang, Qi Zhang, Sihua Tian

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

On-demand cinemas are a new type of offline entertainment venues which have shown the rapid expansion in the recent years. Recommending movies of interest to the potential audiences in on-demand cinemas is keen but challenging because the recommendation scenario is totally different from all the existing recommendation applications including online video recommendation, offline item recommendation and group recommendation. In this paper, we propose a novel spatio-temporal approach called Pegasus. Because of the specific characteristics of on-demand cinema recommendation, Pegasus exploits the POI (Point of Interest) information around cinemas and the content descriptions of movies, apart from the historical movie consumption records of cinemas. Pegasus explores the temporal dynamics and spatial influences rooted in audience behaviors, and captures the similarities between cinemas, the changes of audience crowds, time-varying features and regional disparities of movie popularity. It offers an effective and explainable way to recommend movies to on-demand cinemas. The corresponding Pegasus system has been deployed in some pilot on-demand cinemas. Based on the real-world data from on-demand cinemas, extensive experiments as well as pilot tests are conducted. Both experimental results and post-deployment feedback show that Pegasus is effective.

Original languageEnglish
Title of host publicationProceedings of the 28th ACM International Conference on Information & Knowledge Management
EditorsPeng Cui, Elke Rundensteiner, David Carmel, Qi He, Jeffrey Xu Yu
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages1553-1562
Number of pages10
ISBN (Electronic)9781450369763
DOIs
Publication statusPublished - 2019
EventACM International Conference on Information and Knowledge Management 2019 - Beijing, China
Duration: 3 Nov 20197 Nov 2019
Conference number: 28th
http://www.cikm2019.net/

Conference

ConferenceACM International Conference on Information and Knowledge Management 2019
Abbreviated titleCIKM 2019
CountryChina
CityBeijing
Period3/11/197/11/19
Internet address

Keywords

  • On-demand cinema
  • Recommender system
  • Spatio-temporal effect

Cite this

Xue, T., Jin, B., Li, B., Wang, W., Zhang, Q., & Tian, S. (2019). A spatio-temporal recommender system for on-demand cinemas. In P. Cui, E. Rundensteiner, D. Carmel, Q. He, & J. Xu Yu (Eds.), Proceedings of the 28th ACM International Conference on Information & Knowledge Management (pp. 1553-1562). New York NY USA: Association for Computing Machinery (ACM). https://doi.org/10.1145/3357384.3357888
Xue, Taofeng ; Jin, Beihong ; Li, Beibei ; Wang, Weiqing ; Zhang, Qi ; Tian, Sihua. / A spatio-temporal recommender system for on-demand cinemas. Proceedings of the 28th ACM International Conference on Information & Knowledge Management. editor / Peng Cui ; Elke Rundensteiner ; David Carmel ; Qi He ; Jeffrey Xu Yu. New York NY USA : Association for Computing Machinery (ACM), 2019. pp. 1553-1562
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Xue, T, Jin, B, Li, B, Wang, W, Zhang, Q & Tian, S 2019, A spatio-temporal recommender system for on-demand cinemas. in P Cui, E Rundensteiner, D Carmel, Q He & J Xu Yu (eds), Proceedings of the 28th ACM International Conference on Information & Knowledge Management. Association for Computing Machinery (ACM), New York NY USA, pp. 1553-1562, ACM International Conference on Information and Knowledge Management 2019, Beijing, China, 3/11/19. https://doi.org/10.1145/3357384.3357888

A spatio-temporal recommender system for on-demand cinemas. / Xue, Taofeng; Jin, Beihong; Li, Beibei; Wang, Weiqing; Zhang, Qi; Tian, Sihua.

Proceedings of the 28th ACM International Conference on Information & Knowledge Management. ed. / Peng Cui; Elke Rundensteiner; David Carmel; Qi He; Jeffrey Xu Yu. New York NY USA : Association for Computing Machinery (ACM), 2019. p. 1553-1562.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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AB - On-demand cinemas are a new type of offline entertainment venues which have shown the rapid expansion in the recent years. Recommending movies of interest to the potential audiences in on-demand cinemas is keen but challenging because the recommendation scenario is totally different from all the existing recommendation applications including online video recommendation, offline item recommendation and group recommendation. In this paper, we propose a novel spatio-temporal approach called Pegasus. Because of the specific characteristics of on-demand cinema recommendation, Pegasus exploits the POI (Point of Interest) information around cinemas and the content descriptions of movies, apart from the historical movie consumption records of cinemas. Pegasus explores the temporal dynamics and spatial influences rooted in audience behaviors, and captures the similarities between cinemas, the changes of audience crowds, time-varying features and regional disparities of movie popularity. It offers an effective and explainable way to recommend movies to on-demand cinemas. The corresponding Pegasus system has been deployed in some pilot on-demand cinemas. Based on the real-world data from on-demand cinemas, extensive experiments as well as pilot tests are conducted. Both experimental results and post-deployment feedback show that Pegasus is effective.

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Xue T, Jin B, Li B, Wang W, Zhang Q, Tian S. A spatio-temporal recommender system for on-demand cinemas. In Cui P, Rundensteiner E, Carmel D, He Q, Xu Yu J, editors, Proceedings of the 28th ACM International Conference on Information & Knowledge Management. New York NY USA: Association for Computing Machinery (ACM). 2019. p. 1553-1562 https://doi.org/10.1145/3357384.3357888