Last week my colleague Raz Nissim presented our joint work at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), which was held at Porto, Portugal. This was part of a Yahoo Labs project in which we investigated Recommender Systems for TV shows, under the leadership of Ronny Lempel and in collaboration with Michal Aharon, Eshcar Hillel and Amit Kagian.
Title: Watch-It-Next: A Contextual TV Recommendation System
As consumers of television are presented with a plethora of available programming, improving recommender systems in this domain is becoming increasingly important. Television sets, though, are often shared by multiple users whose tastes may greatly vary. Recommendation systems are challenged by this setting, since viewing data is typically collected and modeled per device, aggregating over its users and obscuring their individual tastes.
This paper tackles the challenge of TV recommendation, specifically aiming to provide recommendations for the next program to watch following the currently watched program the device. We present an empirical evaluation of several recommendation methods over large-scale, real-life TV viewership data. Our extensions of common state-of-the-art recommendation methods, exploiting the current watching context, demonstrate a significant improvement in recommendation quality.
You can download the article from the publisher’s site.
You can also watch here a video (in English) of a previous presentation of our work.
Here are the slides of Raz’s presentation:
Feel free to share your comments below. Thanks!