EPG Content Recommendation in Large Scale: A Case Study on Interactive TV Platform

Recommender systems in TV applications mostly focusing on the recommendation of video-on-demand (VOD) content, though the major part of users' content consumption is realized on linear channel programs, termed EPG content. In this case study we present how we tackled the EPG recommendation task, which exhibits several differences compared to the VOD scenario, including the lack of explicit user feedbacks, the magnitude of cold start problem, as well as data cleaning and feature selection necessary to be applied on raw consumption data. We provide both offline and online model validation. First we showcase the typical approach in machine learning by evaluating models against recall in an offline setting. Then, we investigate in depth the real-world results of the recommendation app using the pre-trained models, and analyze how personalized recommendation influence users watching behavior. The experimentation results are based on our recommender system deployed at a Canadian IPTV service provider using Microsoft Media room middleware.