MIJ 2024, Volume 133, Number 4 (pp. 44 to 49)
[ACTIVE]
Enhancing Linear TV Channel Surfing: A Real-Time Personalized Ranking and Recommendation System with Dual Dynamic Queues
Metadata
- Publisher
- SMPTE
- Doc Type
- Journal Article
- Content Type
- Original Research
- Abbreviated Title
- SMPTE Motion Imaging Jour.
- Volume
- 133, No. 4, pp. 44–49
- Abstract
- The proliferation of linear TV channels complicates content discovery, necessitating more efficient methods for viewers. This paper presents a real-time personalized ranking and recommendation system designed to enhance viewer satisfaction and interaction through dynamic channel surfing. Our approach uses a dual dynamic queue system, comprising Dynamic History Channel Queue (DHCQ) and Dynamic Future Channel Queue (DFCQ), to manage the viewer's interaction effectively. Leveraging advanced deep learning models, we generate “global” and “local” content embeddings and “user” embeddings to ensure real-time updates tailored to content and user time-sensitivities. A ‘look-ahead’ feature further enriches personalization by considering upcoming content on each channel. Preliminary user feedback indicates a strong preference for this system over traditional channel navigation methods, highlighting its potential to transform how viewers engage with linear TV. This paper underscores the system's significant contribution to improving linear TV content discovery and its implications for enhancing viewer satisfaction.
- Publication Date
- 2024-07-01
- DOI
10.5594/JMI.2024/HWAR6964- ISSN
- Print:
1545-0279| Electronic:2160-2492 - Link
- https://doi.org/10.5594/JMI.2024/HWAR6964
- Author(s)
- Ning XuTao Chen
- Keyword(s)
- Linear TV, Channel Ranking, Channel Surfing, Real-Time Personalization, Dynamic Queues
- Copyright
- © 2024 SMPTE
Bibliographic Reference(s)
- Ayaz Nanji, “How Many TV Channels Do People Actually Watch?” Oct 2016. Accessed 15 May 2024. [Online]. Available: https://www.marketingprofs.com/charts/2016/30973/how-many-tv-channels-do-people-actually-watch. EXTERNAL
- Abhinav Ajitsaria, “Build a Recommendation Engine with Collaborative Filtering.” Accessed 15 May 2024. [Online]. Available: https://realpython.com/build-recom-mendation-engine-collaborative-filtering/. EXTERNAL
- Joonseok Lee, Nisarg Kothari, and Paul Natsev, “Content-Based Related Video Recommendations,” Advances in Neural Information Processing Systems (NIPS) Demonstration Track 2, 2016. EXTERNAL
- Yugesh Verma, “A Guide to Building Hybrid Recommendation Systems for Beginners,” Mar. 2024. Accessed 15 May 2024. [Online]. Available: https://analyticsindia-mag.com/a-guide-to-building-hybrid-recommendation-systems-for-beginners/. EXTERNAL
- Paul Covington, Jay Adams, and Emre Sargin, “Deep Neural Networks for YouTube recommendations.” in Proc. 10th ACM Conf. on Recommender Systems, pp. 191–198, 2016. EXTERNAL
- Zhe Zhao, Lichan Hong, Li Wei, Jilin Chen, Aniruddh Nath, Shawn Andrews, Aditee Kumthekar, Maheswaran Sathiamoorthy, Xinyang Yi, and Ed Chi. “Recommending What Video to Watch Next: A Multitask Ranking System,” in Proc. of the 13th ACM Conf. on Recommender Systems, pp. 43–51, 2019. EXTERNAL
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Ning Xu and Tao Chen; Enhancing Linear TV Channel Surfing: A Real-Time Personalized Ranking and Recommendation System with Dual Dynamic Queues, MIJ 2024, Volume 133, Number 4 (pp. 44 to 49); SMPTE, 2024. Available at https://doi.org/10.5594/JMI.2024/HWAR6964
Snippet:
Ning Xu and Tao Chen; Enhancing Linear TV Channel Surfing: A Real-Time Personalized Ranking and Recommendation System with Dual Dynamic Queues, MIJ 2024, Volume 133, Number 4 (pp. 44 to 49); SMPTE, 2024. Available at https://doi.org/10.5594/JMI.2024/HWAR6964
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Ning Xu and Tao Chen; Enhancing Linear TV Channel Surfing: A Real-Time Personalized Ranking and Recommendation System with Dual Dynamic Queues, MIJ 2024, Volume 133, Number 4 (pp. 44 to 49); SMPTE, 2024. Available at https://doi.org/10.5594/JMI.2024/HWAR6964
Snippet:
<span class="citation">Ning Xu and Tao Chen; <cite>Enhancing Linear TV Channel Surfing: A Real-Time Personalized Ranking and Recommendation System with Dual Dynamic Queues</cite>, MIJ 2024, Volume 133, Number 4 (pp. 44 to 49); SMPTE, 2024. Available at <a href="https://doi.org/10.5594/JMI.2024/HWAR6964" target="_blank" rel="noopener">https://doi.org/10.5594/JMI.2024/HWAR6964</a></span>
SMPTE's HTML Pub
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Ning Xu and Tao Chen; Enhancing Linear TV Channel Surfing: A Real-Time Personalized Ranking and Recommendation System with Dual Dynamic Queues, MIJ 2024, Volume 133, Number 4 (pp. 44 to 49); SMPTE, 2024
doi: 10.5594/JMI.2024/HWAR6964
url: https://doi.org/10.5594/JMI.2024/HWAR6964
doi: 10.5594/JMI.2024/HWAR6964
url: https://doi.org/10.5594/JMI.2024/HWAR6964
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<li> Ning Xu and Tao Chen; <cite id="bib-10-5594-jmi-2024-hwar6964">Enhancing Linear TV Channel Surfing: A Real-Time Personalized Ranking and Recommendation System with Dual Dynamic Queues</cite>, MIJ 2024, Volume 133, Number 4 (pp. 44 to 49); SMPTE, 2024 <span class="doi">10.5594/JMI.2024/HWAR6964</span> </li>