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A unique challenge for e-commerce recommendation is that customers are ofteninterested in products that are more advanced than their already purchasedproducts, but not reversed. The few existing recommender systems modelingunidirectional sequence output a limited number of categories or continuousvariables. To model the ordered sequence, we design the first recommendationsystem that both embed purchased items with Word2Vec, and model the sequencewith stateless LSTM RNN. The click-through rate of this recommender system inproduction outperforms its solely Word2Vec based predecessor. Developed in2017, it was perhaps the first published real-world application that makesdistributed predictions of a single machine trained Keras model on Spark slavenodes at a scale of more than 0.4 million columns per row.

Order Matters at Fanatics Recommending Sequentially Ordered Products by LSTM Embedded with Word2Vec