Interpreting Web Shop User's Behavioral Patterns as Fictitious Explicit Rating for Preference Learning
Abstract
We consider applications of user preference rule learning in marketing. We chose rules because of human-understandability.
We chose fuzzy logic because it enables to order items for recommendation. In this paper we introduce a rule based system equivalent to the Fagin-Lotem-Naor preference system.
We show a multi-user version, introduce induction and compare it to several methods for learning user preference. The methods are based, first, on interpreting e-shop user's behavioral patterns collected by scripts as fictitious explicit rating.
After this we use this (fictitious) explicit rating for content based preference learning. Our main motivation is on recommending for small or medium-sized e-commerce portals.
Due to high competition, users of these portals are not too loyal 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. As a test bed, we have conducted several off-line experiments with real user data from travel agency website confirming competitiveness of our method.