AWESOME - A Data Warehouse-based System for Adaptive Website Recommendations
Summary: AWESOME is a data-warehouse-based framework that continuously captures recommendation feedback to compare heterogeneous recommenders. It closes the loop by adaptively selecting the best-performing algorithm, including via machine learning, for automatic website optimization. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Andreas Thor (University of Leipzig)
- 2. Erhard Rahm (University of Leipzig)
BibTeX Citation
@article{thor_vldb04,
title = {{AWESOME - A Data Warehouse-based System for Adaptive Website Recommendations}},
author = {Thor, Andreas and Rahm, Erhard},
journal = {PVLDB},
series = {{VLDB} '04},
pages = {384--395},
doi = {10.1016/B978-012088469-8.50036-X},
url = {https://doi.org/10.1016/B978-012088469-8.50036-X},
year = {2004}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,231 | REFEREE: An open framework for practical testing of recommender systems using ResearchIndex | 2002 | VLDB | 5.9425753e-05 |
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