Publication at Faculty of Mathematics and Physics |
2013
Abstract
In this paper, we imagine the situation of a typical e-commerce portal employing personalized recommendation. Such website typically receives user feedback from their implicit behavior such as time on page, scrolling etc.
The implicit feedback is generally understood as positive only, however we present several methods how to identify some of the implicit feedback as negative user preference, how to aggregate various feedback types together and how to recommend based on it. We have conducted several off-line experiments with real user data from travel agency website confirming that treating some implicit feedback as negative preference can significantly improve recommendation quality.