Real-time Personalized Ranking and Recommendation System for Linear TV: A Dual Dynamic Queue Approach

Metadata

Publisher: SMPTE — White Plains, NY, USA
Doc Type: Conference Paper
Content Type: Original Research
Volume: 2023, No. 10, pp. 8–10
Abstract: The increasing number of channels on linear TV has amplified the challenge of efficient content discovery for viewers. This paper presents an innovative, real-time personalized ranking and recommendation system designed to address this problem. The objective was to enhance viewer satisfaction and interaction through a personalized, dynamic channel surfing experience. Our approach uses a dual dynamic queue system - the Dynamic History Channel Queue (DHCQ) and the Dynamic Future Channel Queue (DFCQ), each serving a unique purpose in managing the viewer's channel interaction. — Advanced deep learning models reinforce these queues’ dynamism by generating ‘global’ and ‘local’ content embeddings and ‘user’ embeddings. These embeddings are used to provide real-time updates, considering both timestamps and the time-sensitivities of the content and viewer. A ‘look- ahead’ feature was integrated to account for future content on each channel, adding another layer of personalization. — Preliminary user feedback highlighted a strong interest in this new form of personalized channel surfing, with a majority of respondents indicating a preference for our system over traditional channel navigation methods. The results show that our proposed solution could potentially change the way users interact with the ever-growing number of channels on linear TV.
Publication Date: 2023-10-16
DOI: 10.5594/M002000
ISBN: Print: 978-1-61482-964-5
Link: https://doi.org/10.5594/M002000
Author(s): Ning XuPh.D. Adeia Inc.
Tao ChenPh.D. Adeia Inc.
Keyword(s): Linear TV, Channel Ranking, Channel Surfing, Real-Time Personalization, Dynamic Queues, User Preferences, Personalization
Copyright: © 2023 Society of Motion Picture and Television Engineers, Inc.

Bibliographic Reference(s)

Covington, Paul, Jay Adams, and Emre Sargin. "Deep neural networks for youtube recommendations." Proceedings of the 10th ACM conference on recommender systems. 2016. EXTERNAL
Verma, Yugesh. A Guide to Building Hybrid Recommendation Systems for Beginners , https://analyticsindiamag.com/a-guide-to-building-hybrid-recommendation-systems-for-beginners/ EXTERNAL
Ajitsaria, Abhinav. Build a recommendation-engiine with collaborative filtering. https://realpython.com/build-recommendation-engine-collaborative-filtering/ EXTERNAL
Nanji, Ayaz. How Many TV Channels Do People Actually Watch? https://www.marketingprofs.com/charts/2016/30973/how-many-tv-channels-do-people-actually-watch EXTERNAL
Zhe Zhao, Lichan Hong, Li Wei, Jilin Chen, Aniruddh Nath, Shawn Andrews, Aditee Kumthekar, Maheswaran Sathiamoorthy, Xinyang Yi, and Ed Chi. 2019. Recommending what video to watch next: a multitask ranking system. In Proceedings of the 13th ACM Conference on Recommender Systems (RecSys’19) EXTERNAL
Lee, Joonseok, Nisarg Kothari, and Paul Natsev. “Content-based related video recommendations.” Advances in Neural Information Processing Systems (NIPS) Demonstration Track (2016): 2. EXTERNAL

Source Data (JSON)

Full registry record with provenance metadata. Open directly: /api/doc/10.5594-M002000.json

Reference Tree

Explore all references and references to this document, as a navigable tree.

Open Reference Tree

Reference this Doc

Plain text (ISO 690 compliant)

Preview:

Ning Xu and Tao Chen; Real-time Personalized Ranking and Recommendation System for Linear TV: A Dual Dynamic Queue Approach, SMPTE Meetings and Conferences ( October 2023); SMPTE, 2023, ISBN: 978-1-61482-964-5. Available at https://doi.org/10.5594/M002000

Snippet:

Ning Xu and Tao Chen; Real-time Personalized Ranking and Recommendation System for Linear TV: A Dual Dynamic Queue Approach, SMPTE Meetings and Conferences ( October 2023); SMPTE, 2023, ISBN: 978-1-61482-964-5. Available at https://doi.org/10.5594/M002000

HTML (ISO 690 compliant)

Preview:

Snippet:

<span class="citation">Ning Xu and Tao Chen; <cite>Real-time Personalized Ranking and Recommendation System for Linear TV: A Dual Dynamic Queue Approach</cite>, SMPTE Meetings and Conferences ( October 2023); SMPTE, 2023, ISBN: 978-1-61482-964-5. Available at <a href="https://doi.org/10.5594/M002000" target="_blank" rel="noopener">https://doi.org/10.5594/M002000</a></span>

SMPTE's HTML Pub

Preview:

Snippet:

<li>
Ning Xu and Tao Chen; <cite id="bib-10-5594-m002000">Real-time Personalized Ranking and Recommendation System for Linear TV: A Dual Dynamic Queue Approach</cite>, SMPTE Meetings and Conferences ( October 2023); SMPTE, 2023, ISBN: 978-1-61482-964-5
<span class="doi">10.5594/M002000</span>
</li>