Abstract
Our research is focused on interpreting user preference from his/her implicit behavior. There are many types of relevant behavior e.g. time on page, scrolling, clickstream etc. which we will further denote as Relevant Behavior Types (RBT).
RBT s varies both in quality and incidence and thus we might need different approaches to process them. In this early work we focus on how to derive user preference from each RBT separately.
We selected number of common indicators, design two novel e-commerce specific RBT interpreting methods and conducted series of offline experiments. After the off-line evaluation an A/B test on the real-world users of a travel agency was conducted comparing best off-line method with simple binary feedback.
The experiments, although preliminary, showed importance of considering multiple RBTs together.