M. Jeffrey Mei
ABSTRACT
We apply a transformer using sequential browse history to generate next-item product recommendations. Interpreting the learned item embeddings, we show that the model is able to implicitly learn price, popularity, style and functionality attributes without being explicitly passed these features during training. Our real-life test of this model on Wayfair’s different international stores show mixed results (but overall win). Diagnosing the cause, we identify a useful metric (average number of customers browsing each product) to ensure good model convergence. We also find limitations of using standard metrics like recall and nDCG, which do not correctly account for the positional effects of showing items on the Wayfair website, and empirically determine a more accurate discount factor.
Supplemental Material
- Hao Jiang, Aakash Sabharwal, Adam Henderson, Diane Hu, and Liangjie Hong. 2019. Understanding the Role of Style in E-commerce Shopping. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 3112–3120.Google Scholar
Digital Library
- Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive Sequential Recommendation. In 2018 IEEE International Conference on Data Mining (ICDM). IEEE, 197–206.Google Scholar
- Leland McInnes, John Healy, Nathaniel Saul, and Lukas Großberger. 2018. UMAP: Uniform Manifold Approximation and Projection. Journal of Open Source Software 3, 29 (2018).Google ScholarCross Ref
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention Is All You Need. Advances in neural information processing systems 30 (2017).Google ScholarDigital Library
- Ellen M Voorhees 1999. The TREC-8 Question Answering Track Report. In TREC, Vol. 99. 77–82.Google Scholar
Index Terms
- A Lightweight Transformer for Next-Item Product Recommendation
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