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)

Paper ID: hf8b6493ad1d1344d
Venue: VLDB
Year: 2004
Pagerank: 4.9793485e-05
Overall Rank: 13,083 | 12.04%
DOI: 10.1016/B978-012088469-8.50036-X

Incoming Non-self Citations Over Time

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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}
}

Incoming Citations (Sorted by Pagerank)

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Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
6,360	REFEREE: An open framework for practical testing of recommender systems using ResearchIndex	2002	VLDB	5.8092399e-05

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