Recommendation Systems: A Probabilistic Analysis

A recommendation system tracks past actions of a group of users to make recommendations to individual members of the group. The growth of computer-mediated marketing and commerce has led to increased interest in such systems. We introduce a simple analytical framework for recommendation systems, including a basis for defining the utility of such a system. We perform probabilistic analyses of algorithmic methods within this framework. These analyses yield insights into how much utility can be derived from the memory of past actions and on how this memory can be exploited.

Authors

• Ravi Kumar 0001
• Prabhakar Raghavan
• Sridhar Rajagopalan
• Andrew Tomkins

Keywords

• Algorithm design and analysis
• Collaboration
• Filtering algorithms
• Random access memory
• Books
• Business
• Microwave integrated circuits
• Information filtering
• Information filters
• Electrical capacitance tomography
• Recommender Systems
• Probabilistic Model
• Analysis Algorithm
• Simple Algorithm
• Algorithm Design
• Filtering Algorithm
• User Preferences
• User Satisfaction
• Cluster C
• Science Fiction
• Collaborative Filtering
• Item Clusters
• Recommendation Algorithm
• User Density
• Bad Events