Darwin & goliath: A white-label recommender-system as-a-service with automated algorithm-selection

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Abstract

Recommendations-as-a-Service (RaaS) ease the process for small and medium-sized enterprises (SMEs) to offer product recommendations to their customers. Current RaaS, however, suffer from a one-size-fits-all concept, i.e. they apply the same recommendation algorithm for all SMEs. We introduce Darwin & Goliath, a RaaS that features multiple recommendation frameworks (Apache Lucene, TensorFlow, …), and identifies the ideal algorithm for each SME automatically. Darwin & Goliath further offers per-instance algorithm selection and a white label feature that allows SMEs to offer a RaaS under their own brand. Since November 2018, Darwin & Goliath has delivered more than 1m recommendations with a CTR = 0.5%.

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Beel, J., Griffin, A., & O’Shea, C. (2019). Darwin & goliath: A white-label recommender-system as-a-service with automated algorithm-selection. In RecSys 2019 - 13th ACM Conference on Recommender Systems (pp. 534–535). Association for Computing Machinery, Inc. https://doi.org/10.1145/3298689.3347059

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