RecBench: Benchmarks for Evaluating Performance of Recommender System Architectures

@article{Levandoski2011RecBenchBF,
  title={RecBench: Benchmarks for Evaluating Performance of Recommender System Architectures},
  author={Justin J. Levandoski and Michael D. Ekstrand and Michael Ludwig and A. Eldawy and M. Mokbel and J. Riedl},
  journal={Proc. VLDB Endow.},
  year={2011},
  volume={4},
  pages={911-920}
}
Traditionally, recommender systems have been “hand-built”, implemented as custom applications hard-wired to a particular recommendation task. Recently, the database community has begun exploring alternative DBMS-based recommender system architectures, whereby a database both stores the recommender system data (e.g., ratings data and the derived recommender models) and generates recommendations using SQL queries. In this paper, we present a comprehensive experimental comparison of both… Expand
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References

SHOWING 1-10 OF 26 REFERENCES
Hybrid Recommender Systems: Survey and Experiments
  • R. Burke
  • Computer Science
  • User Modeling and User-Adapted Interaction
  • 2004
Item-based collaborative filtering recommendation algorithms
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
Toward a personal recommender system
FlexRecs: expressing and combining flexible recommendations
Google news personalization: scalable online collaborative filtering
MovieLens unplugged: experiences with an occasionally connected recommender system
...
1
2
3
...