When to recommend: A new issue on TV show recommendation

Abstract

Recommender systems have gained much attention in both research and industry communities, and have been actively researched for the last decade. However, recommendation techniques for TV shows have not been actively researched despite TV's importance. It is because TV show recommendation has two unique and notable characteristics: (1) items (i.e., TV shows) are available only for a certain time period and (2) user cannot watch two different shows at the same time. Due to the different characteristics, TV recommender system should be able to recommend item in online time, and deciding the recommendation timing becomes an important issue for TV show recommender system. Developing such a system raises several technical challenges: (1) Since the time conditions of TV shows such as watching time and remaining time affect on how much the user is attracted to the show, recommendation must consider the time conditions as well as users' preferences on items. (2) The cost of inaccurate recommendations (or inaccurate timing) is higher than other domains, because a recommendation involves blocking a part of screen. This paper proposes a novel recommender system for TV shows called ShowTime, which determines the timing as well as the items for recommendation. In our extensive experiments on a real-world data, the proposed TV show recommender system, ShowTime, demonstrates promising results in terms of accuracy and the cost management. (C) 2014 Elsevier Inc. All rights reserved.