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

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).

Automated summary

This study focuses on predicting the next TV program by analyzing the time sequences of TV viewing, aiming to better understand the viewing preferences and habits of TV viewers. The research conducted experiments using various methods, ultimately demonstrating high prediction accuracy.