Abstract
The task “recommend a video to watch next?” has been in the focus of recommender systems’ research for a long time. However, adequately exploiting the clues hidden in the sequences of actions of user sessions in order to reveal users’ short-term intentions moved only recently into the focus of research. Based on a real-world application scenario, in this paper, we propose a Markov Chain-based transition probability matrix to efficiently reveal the short-term preferences of individuals. We experimentally evaluated our proposed method by comparing it against state-of-the-art algorithms in an offline as well as a live evaluation setting. In both cases our method not only demonstrated its superiority over its competitors, but exposed a clearly stronger engagement of users on the platform. In the online setting, our method improved the click-through rate by up to 93.61%. This paper therefore contributes real-world evidence for improving the recommendation effectiveness, by considering sequence-awareness, since capturing the short-term preferences of users is crucial in the light of items with a short life span such as tv programs (news, tv shows, etc.).