Adaptive website recommendations with AWESOME

Recommendations are crucial for the success of large websites. While there are many ways to determine recommendations, the relative quality of these recommenders depends on many factors and is largely unknown. We present the architecture and implementation of AWESOME (Adaptive website recommendations), a data warehouse-based recommendation system. It allows the coordinated use of a large number of recommenders to automatically generate website recommendations. Recommendations are dynamically selected by efficient rule-based approaches utilizing continuously measured user feedback on presented recommendations. AWESOME supports a completely automatic generation and optimization of selection rules to minimize website administration overhead and quickly adapt to changing situations. We propose a classification of recommenders and use AWESOME to comparatively evaluate the relative quality of several recommenders for a sample website. Furthermore, we propose and evaluate several rule-based s...

• Type: Conference
• Year: 2005
• Where: VLDB
• Authors: Andreas Thor, Nick Golovin, Erhard Rahm
• Keywords: Adaptive Web Recommendations, Adaptive Website Recommendations, Database, Promising Recommendations, VLDB 2005