Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Effective personalization based on association rule discovery from web usage data
Proceedings of the 3rd international workshop on Web information and data management
Communications of the ACM - Robots: intelligence, versatility, adaptivity
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
The Long Tail: Why the Future of Business Is Selling Less of More
The Long Tail: Why the Future of Business Is Selling Less of More
Research Note: The Influence of Recommendations and Consumer Reviews on Evaluations of Websites
Information Systems Research
Web Personalization as a Persuasion Strategy: An Elaboration Likelihood Model Perspective
Information Systems Research
Information Systems Research
Special Section: Customer-Centric Information Systems
Journal of Management Information Systems
Journal of Management Information Systems
Persuasion in Recommender Systems
International Journal of Electronic Commerce
International Journal of Electronic Commerce
Recommendation Agents for Electronic Commerce: Effects of Explanation Facilities on Trusting Beliefs
Journal of Management Information Systems
Assessing the impact of internet agent on end users' performance
Decision Support Systems
The effect of context-aware recommendations on customer purchasing behavior and trust
Proceedings of the fifth ACM conference on Recommender systems
Incremental Collaborative Filtering recommender based on Regularized Matrix Factorization
Knowledge-Based Systems
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
Explaining the user experience of recommender systems
User Modeling and User-Adapted Interaction
Proceedings of the 14th Annual International Conference on Electronic Commerce
Applying the learning rate adaptation to the matrix factorization based collaborative filtering
Knowledge-Based Systems
Journal of Management Information Systems
Movie recommender system for profit maximization
Proceedings of the 7th ACM conference on Recommender systems
International Journal of Business Information Systems
Hi-index | 0.00 |
Online retailers are increasingly using information technologies to provide value-added services to customers. Prominent examples of these services are online recommender systems and consumer feedback mechanisms, both of which serve to reduce consumer search costs and uncertainty associated with the purchase of unfamiliar products. The central question we address is how recommender systems affect sales. We take into consideration the interaction among recommendations, sales, and price. We then develop a robust empirical model that incorporates the indirect effect of recommendations on sales through retailer pricing, potential simultaneity between sales and recommendations, and a comprehensive measure of the strength of recommendations. Applying the model to a panel data set collected from two online retailers, we found that the strength of recommendations has a positive effect on sales. Moreover, this effect is moderated by the recency effect, where more recently released recommended items positively affect the cross-selling efforts of sellers. We also show that recommender systems help to reinforce the long-tail phenomenon of electronic commerce, and obscure recommendations positively affect cross-selling. We also found a positive effect of recommendations on prices. These results suggest that recommendations not only improve sales but they also provide added flexibility to retailers to adjust their prices. A comparative analysis reveals that recommendations have a higher effect on sales than does consumer feedback. Our empirical results show that providing value-added services, such as digital word of mouth and recommendations, allows retailers to charge higher prices while at the same time increasing demand by providing more information regarding the quality and match of products.