Mobile Product Browsing Using Bayesian Retrieval

Reacting to technological advances in the domain of mobile devices, many traditionally desktop-bound applications now are ready to make the transition into the mobile world. Especially mobile shopping applications promise a large potential for commercial. However, in order to work on the limited screen estate even of modern devices, traditional category-based browsing approaches to online shopping have to be rethought. In this paper we design an innovative approach to intuitively guide users through product databases based on Bayesian probability modeling for navigational purposes. Our navigation model is focused on feedback and inspired by content-based retrieval techniques. Moreover, we exploit new features of today's devices like touch screens to ease interaction. Due to the novel interface-related simplicity, our system supports users in their decision process while demanding only minimal cognitive load. We outline the theoretical foundations and the design space of such a system and evaluate its retrieval effectiveness using real-world data sets. In fact, we show that using our probabilistic navigation model about 98% of all searches can be completed successfully with an average of only 3 rounds of feedback on the 4th displayed screen.