This study examines the behavioral aspect of improving the recommendation agent-consumer relationship, utilizing a model of internal information search for unplanned purchases prompted by a recommendation from a collaborative filtering agent. The model describes how consumers update their beliefs about a product upon receiving a recommendation and identifies the factors affecting the increase in the product's expected utility after the recommendation. A Monte Carlo simulation derives propositions regarding how these factors influence the effectiveness of recommendations. Broadly, the marginal value of recommendation depends on the preference structure of the recipient, the attributes of the product on which the recommendation is based, and the characteristics of the population of consumers. The major managerial implication is that retailers should include more information in recommendations when the products are less common or when there is a large variability of user tastes.

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Buying behavior,Internal information search,Marginal value of recommendation,Monte Carlo simulation,On-line product recommendations

• Computer and Information Sciences - Natural Sciences
• Economics and Business - Social Sciences