|Title||Mobile Product Browsing using Bayesian Retrieval|
|Publication Type||Conference Paper|
|Year of Publication||2010|
|Authors||Lofi, C., C. Nieke, and W. - T. Balke|
|Conference Name||12th IEEE Conference on Commerce and Enterprise Computing (CEC)|
|Conference Location||Shanghai, China|
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 brows-ing 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 model-ing 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.
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