Abstrakt
In this paper, we present our vision and some initial experiments on how to anticipate significance, similarity or polarity of various types of (preferably implicit) user feedback and how to form individual user preference for recommendation. Throughout the corporate web, we can observe the same patterns or actions in user behavior (e.g. page-view, amount of scrolling, rating or purchasing).
Recorded user behavior - user feedback - is often used as base for personalized recommendation, but the connection between the feedback and user preference is often unclear or noisy. Our goal is to analyze user behavior in order to understand its relation to the user preference.
We report on some initial experiments on a real-world ecommerce application. We describe our new models and methods how to combine various feedback types and how to learn user preferences.