Human-website interaction monitoring in recommender systems

The purpose of this paper is to investigate how a study group consisting of 85 participants interact with selected online stores and how the interactions correlate with interest in products for each store. This work uses a quantitative research methodology involving a dedicated tool for implicit monitoring of human-website interaction, instrumented for selected stores and registering product interest for the benefit of recommender systems. One of the findings was that to predict product interest it seems a good idea to start with monitoring scrolling activities, mouse usage and time spent on a website and its sections. Rich product information played a crucial role in shaping user interest. For all stores in the study, a misclassification rate of 28.7% was achieved in a CART model, while modeling for particular stores, it varied from 39.3% to 24.8%, and we feel that it reflected different page layouts in stores. Models built to represent individual behavior patterns of most active study participants varied in terms of misclassification rates from 17% to 26%, and the analysis suggested that individual preference modeling could be considered for recommender systems, in particular for key customers or customer groups. The study leads to insights into online store user behavior and product interest prediction, and as a result to possible implications for recommender systems design, including ergonomics optimization and interaction personalization.