Integrating visualization and multi-attribute utility theory for online product selection

Effectively selling products online is a challenging task. Today's product domains often contain a dizzying variety of brands and models with highly complex sets of characteristics. This paper addresses the problem of supporting product search and selection in domains containing large numbers of alternatives with complex sets of features. A number of online shopping websites provide product choice assistance by making direct use of Multi-Attribute Utility Theory (MAUT). While the MAUT approach is appealing due to its solid theoretical foundations, there are several reasons that it does not fit well with people's decision making behavior. This paper presents an approach designed to better fit with people's natural decision making process. The system is called VMAP for Visualizing Multi-Attribute Preferences. VMAP provides on one screen both a multi-attribute preference tool (MAP-tool) and a product visualization tool (V-tool). The product visualization tool displays the set of available products, with each product displayed as a point in a 3D attribute space. By viewing the product space, users can gain an overview of the range of available products, as well as an understanding of the relationships between their attributes. The MAP-tool integrates expression of preferences and filter conditions, which are then immediately reflected in the V-tool display. In this way, the user can immediately see the consequences of his expressed preferences on the product space. The VMAP system is evaluated on a number of factors by comparing users' subjective ratings of the system to those of a more traditional MAUT product selection tool. The results show that while VMAP is somewhat more difficult to use than a traditional MAUT product selection tool, it provides better flexibility, provides the ability to more effectively explore the product domain, and produces more confidence in the selected product.