Modelling User Preferences from Implicit Preference Indicators via Compensational Aggregations

摘要

In our work, we focus on recommending for small or medium-sized e-commerce portals. Due to high competition, users of these portals lack loyalty and e.g. refuse to register or provide any/enough explicit feedback. Furthermore, products such as tours, cars or furniture have very low average consumption rate preventing us from tracking unregistered user between two consecutive purchases. Recommending on such domains proves to be very challenging, yet interesting research task. We will introduce new method for learning user preferences based on their implicit feedback. The method is based on aggregating various types of implicit feedback with parameterized fuzzy T-norms and S-norms. We have conducted several off-line experiments with real user data from travel agency confirming competitiveness of our method, however further optimizing and on-line experiments should be conducted in the future work.