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RecBench: Benchmarks for Evaluating Performance of Recommender System Architectures

Summary: Defines RecBench benchmarks for evaluating DBMS-based vs hand-built recommender architectures. Evaluates MultiLens vs RecStore on MovieLens 10M and Netflix 100M, showing hand-built excels in model-building and pure recommendations, while DBMS-based shines in filtered/hybrid tasks. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10510
Venue
VLDB
Year
2011
Pagerank
5.093636e-05
Overall Rank
12,406 | 14.89%
DOI
10.14778/3402707.3402714

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BibTeX Citation

@article{levandoski_vldb11,
        title = {{RecBench: Benchmarks for Evaluating Performance of Recommender System Architectures}},
        author = {Levandoski, Justin J. and Eldawy, Ahmed and Ekstrand, Michael D. and Mokbel, Mohamed F. and Ludwig, Michael J. and Riedl, John T.},
        journal = {PVLDB},
        series = {{VLDB} '11},
        volume = {4},
        number = {11},
        pages = {911--921},
        doi = {10.14778/3402707.3402714},
        url = {https://doi.org/10.14778/3402707.3402714},
        year = {2011}
}

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