Context-Tree Recommendation vs Matrix-Factorization: Algorithm Selection and Live Users Evaluation

Authors

  • Stéphane Martin École Polytechnique Fédérale de Lausanne
  • Boi Faltings École Polytechnique Fédérale de Lausanne
  • Vincent Schickel Prediggo SA

DOI:

https://doi.org/10.1609/aaai.v33i01.33019534

Abstract

We describe the selection, implementation and online evaluation of two e-commerce recommender systems developed with our partner company, Prediggo. The first one is based on the novel method of Bayesian Variable-order Markov Modeling (BVMM). The second, SSAGD, is a novel variant of the Matrix-Factorization technique (MF), which is considered state-of-the-art in the recommender literature.

We discuss the offline tests we carried out to select the best MF variant, and present the results of two A/B tests performed on live ecommerce websites after the deployment of the new algorithms. Comparing the new recommenders and Prediggo’s proprietary algorithm of Ontology Filtering, we show that the BVMM significantly outperforms the two others in terms of CTR and prediction speed, and leads to a strong increase in recommendation-mediated sales. Although MF exhibits reasonably good accuracy, the BVMM is still significantly more accurate and avoids the high memory requirements of MF. This scalability is essential for its application in online businesses.

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Published

2019-07-17

How to Cite

Martin, S., Faltings, B., & Schickel, V. (2019). Context-Tree Recommendation vs Matrix-Factorization: Algorithm Selection and Live Users Evaluation. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9534-9540. https://doi.org/10.1609/aaai.v33i01.33019534

Issue

Section

IAAI Technical Track: Emerging Papers