Скачать или смотреть Session 7: A Lightweight Transformer for Next Item Product Recommendation

RecSys 2022 by Jeffrey Mei (Wayfair LLC, United States), Cole Zuber (Wayfair LLC, United States), Yasaman Khazaeni (Wayfair LLC, United States)

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.