Using a Community of Knowledge to Build Intelligent Agents

The modeling of individual consumer preference can be aided by incorporating others' opinions which contain information above and beyond identified product attributes. The value of others' opinions is tested using two empirical data sets. The results indicate that incorporating others' opinions into an attribute-based model can reduce systematic error and increase predictive accuracy by serving as a proxy for missing information (e.g., undiscovered attributes or attribute interactions, sensory or experiential aspects of the product, as well as advertising or word of mouth effects). Additionally, modeling individual preference based on others' opinions alone is shown to predict as well or better than traditional multiattribute models thus bypassing the need for defining a product attribute space.     

“I love it” or “I hate it”? The positivity effect in stated preferences for agent evaluation

Consumers often interact with agents to obtain advice about products and services. A consumer’s evaluation of an agent as a source of personalized advice depends, in part, on the extent to which the consumer believes the agent knows and shares her tastes. In this research, we show a positivity effect in the agent evaluation process, whereby consumers perceive alternatives they love (compared to hate) to be more informative to agents about their tastes, and hence more diagnostic to agents for predicting their future evaluations. Further, we show that this positivity effect is moderated by the agent’s level of agreement with the consumer, and is driven by the greater accessibility of information about loved, compared to hated, alternatives. We discuss the implications of these results for interpersonal judgments and agent choice.