Deep inventory time translation to improve recommendations for real-world retail
Bobby Prévost, Jonathan Laflamme Janssen, Jaime R. Camacaro, Carolina Bessega · 2018
Recommender systems are an important component in the retail industry, but the constantly renewed inventory of many companies makes it difficult to aggregate enough data to fully harness the benefits of such systems. In this paper, we describe a technique that significantly improves the accuracy of the recommendations, validated on a real store transaction history, by performing a time translation that maps out-of-stock items to similar items that are currently in stock using deep features of the products. This greatly reduces the dimension of the item-item interactions matrix while preserving all the dataset entries, which mitigates the sparsity of the dataset, and provides an original solution to the cold-start problem. We also improve the coverage at no accuracy cost by favouring less popular items within a small radius in the feature space while applying the time translation mapping. Finally, by modelling item-item rather that user-item correlations, we are able to update the recommendations for a given user in real-time, without re-training, as the user's history receives new entries.