Implications of Consumer Information Behaviour to Construct Utility-based Recommender Systems: A Prototypical Study

Product recommender systems aim to support consumers in making buying decisions. However, such a support requires considering the consumer behaviour in making buying decisions. In this paper, we deduce design requirements for utility-based recommender systems from the theory of consumer information behaviour and present empirically findings from experiments conducted with a prototypical implementation of the proposed requirements. The empirical examination shows that our recommender system has a high predictive validity

Estimating Optimal Recommendation Set Sizes for Individual Consumers

Online consumers must burrow through vast piles of product information to find the best match to their preferences. This has boosted the popularity of recommendation agents promising to decrease consumers\u27 search costs. Most recent work has focused on refining methods to find the best products for a consumer. The question of how many of these products the consumer actually wants to see, however, is largely unanswered. This paper develops a novel procedure based on signal detection theory to estimate the number of recommendable products. We compare it to a utility exchange approach that has not been empirically examined so far. The signal detection approach showed very good predictive validity in two laboratory experiments, clearly outperforming the utility exchange approach. Our theoretical findings, supported by the experimental evidence, indicate conceptual inconsistencies in the utility exchange approach. Our research offers significant implications for both theory and practice of modeling consumer choice behavior

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.Visualization, multi-attribute utility theory, choice assistance, online shopping, usability evaluation