Reducing buyer search costs: implications for electronic marketplaces
Management Science - Special issue: Frontier research on information systems and economics
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
A graph model for E-commerce recommender systems
Journal of the American Society for Information Science and Technology
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
A Recommender for Targeted Advertisement of Unsought Products in E-Commerce
CEC '05 Proceedings of the Seventh IEEE International Conference on E-Commerce Technology
A semantic-expansion approach to personalized knowledge recommendation
Decision Support Systems
International Journal of Electronic Commerce
International Journal of Electronic Commerce
Online customers' cognitive differences and their impact on the success of recommendation agents
Information and Management
Empirical Analysis of the Impact of Recommender Systems on Sales
Journal of Management Information Systems
Modeling Consumer Purchasing Behavior in Social Shopping Communities with Clickstream Data
International Journal of Electronic Commerce
Effect of user-generated content on website stickiness: the case of social shopping communities
Proceedings of the 14th Annual International Conference on Electronic Commerce
Proceedings of the 13th International Conference on Electronic Commerce
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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.