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.