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

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.

Published

2023-10

Content type

Original Research

Keywords

Linear TV, Channel Ranking, Channel Surfing, Real-time Personalization, Dynamic Queues, User Preferences, Personalized Content Delivery

DOI

10.5594/M002000

ISBN

978-1-61482-964-5