Abstrakt
Our work is generally focused on recommending for small or medium-sized e-commerce portals, where we are facing scarcity of explicit feedback, low user loyalty, short visit times or low number of visited objects. In this paper, we present a novel approach to use specific user behavior as implicit feedback, forming binary relations between objects.
Our hypothesis is that if user select some object from the list of displayed objects, it is an expression of his/her binary preference between selected and other shown objects. These relations are expanded based on content-based similarity of objects forming partial ordering of objects.
Using these relations, it is possible to alter any list of recommended objects or create one from scratch. We have conducted several off-line experiments with real user data from a Czech e-commerce site with keyword based VSM and SimCat recommenders.
Experiments confirmed competitiveness of our method, however on-line A/B testing should be conducted in the future work.