<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph Lofi</style></author><author><style face="normal" font="default" size="100%">Christian Nieke</style></author><author><style face="normal" font="default" size="100%">Wolf-Tilo Balke</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mobile Product Browsing using Bayesian Retrieval</style></title><secondary-title><style face="normal" font="default" size="100%">12th IEEE Conference on Commerce and Enterprise Computing (CEC)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">mobile shopping. probabilistic retrieval</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2010</style></date></pub-dates></dates><urls><related-urls><url><style face="normal" font="default" size="100%">http://www.ifis.cs.tu-bs.de/sites/default/files/biblio/fulltext_25580.pdf</style></url></related-urls></urls><pub-location><style face="normal" font="default" size="100%">Shanghai, China</style></pub-location><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record></records></xml>