Incorporating time-interval sequences in linear TV for next-item prediction

Veronika Bogina, Yuri Variat, Tsvi Kuflik, Eyal Dim

Research output: Contribution to journalArticlepeer-review

Abstract

As linear TV remains a major source for media consumption, multiple stakeholders such as broadcasters and advertisers are interested in the prediction of the next programs to be watched by TV viewers. Such predictions are quite challenging given the nature of the domain, where viewing TV is not just an individual activity but is also influenced by various contextual factors. We aim to address this challenge by integrating temporal aspects of linear TV – in the form of sequences – into the next program prediction process. A user profile is built from sequences of 24-hour TV programs’ views, at intervals of 15 min. Such profiles allow us to capture viewing preferences and sequential patterns, for predicting the next program/genre/category to be viewed at any time. We conducted several experiments using naive approaches, hidden Markov models and deep learning, juggling between accuracy and interpretability of the model. The precision@1 results were extremely promising (0.836 for next category prediction with LSTM, comparing to 0.57 an 0.34 with Naive approaches, and 0.366 with hidden Markov models).

Original languageEnglish
Article number116284
JournalExpert Systems with Applications
Volume192
DOIs
StatePublished - 15 Apr 2022

Bibliographical note

Funding Information:
This research was supported by THE ISRAEL SCIENCE FOUNDATION (grant No. 262/2017 ) and was partially supported by the “ InfoMedia ” project, a project of the Israeli Innovation Authority, the Ministry of Economy and Industry.

Publisher Copyright:
© 2021 Elsevier Ltd

Keywords

  • Recommender systems
  • Sequences
  • TV domain

ASJC Scopus subject areas

  • Engineering (all)
  • Computer Science Applications
  • Artificial Intelligence

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