Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Commercial Applications of Machine Learning for Personalized Wireless Portals
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Recommender systems and their impact on sales diversity
Proceedings of the 8th ACM conference on Electronic commerce
Acquiring and Revising Preferences in a Critique-Based Mobile Recommender System
IEEE Intelligent Systems
The value of personalised recommender systems to e-business: a case study
Proceedings of the 2008 ACM conference on Recommender systems
Enabling intelligent content discovery on the mobile internet
IAAI'07 Proceedings of the 19th national conference on Innovative applications of artificial intelligence - Volume 2
Persuasive online-selling in quality and taste domains
EC-Web'06 Proceedings of the 7th international conference on E-Commerce and Web Technologies
Off the beaten track: a mobile field study exploring the long tail of tourist recommendations
Proceedings of the 12th international conference on Human computer interaction with mobile devices and services
Recommending based on rating frequencies
Proceedings of the fourth ACM conference on Recommender systems
Userrank for item-based collaborative filtering recommendation
Information Processing Letters
Accuracy improvements for multi-criteria recommender systems
Proceedings of the 13th ACM Conference on Electronic Commerce
Case study on the business value impact of personalized recommendations on a large online retailer
Proceedings of the sixth ACM conference on Recommender systems
Proceedings of the 2013 international conference on Intelligent user interfaces
Cost-Aware Collaborative Filtering for Travel Tour Recommendations
ACM Transactions on Information Systems (TOIS)
How should I explain? A comparison of different explanation types for recommender systems
International Journal of Human-Computer Studies
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This paper summarizes the initial findings of an experimental evaluation of how recommender systems affect the buying behavior of online customers. The study was conducted in the context of a large-scale, commercial Mobile Internet platform, from which end users can download games to their mobile phones. Item recommendations were presented to platform visitors in different navigational situations; the recommendation lists were either determined with the help of different recommendation algorithms or based on nonpersonalized ranking techniques. The study is based on a sample of more than 155,000 different customers who visited the portal during a four week evaluation period. The analysis revealed that the use of personalized recommendations instead of non-personalized ones leads to a significant increase in viewed and sold items in different navigational situations and to an overall sales increase.