Deep inventory time translation to improve recommendations for real-world retail

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.

[1]  Jeff Donahue,et al.  Visual Search at Pinterest , 2015, KDD.

[2]  Dik Lun Lee,et al.  Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba , 2018, KDD.

[3]  Heng-Tze Cheng,et al.  Wide & Deep Learning for Recommender Systems , 2016, DLRS@RecSys.

[4]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[5]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Lina Yao,et al.  Deep Learning Based Recommender System , 2017, ACM Comput. Surv..

[7]  Tat-Seng Chua,et al.  Fast Matrix Factorization for Online Recommendation with Implicit Feedback , 2016, SIGIR.

[8]  Hsuan-Tien Lin,et al.  Compatibility Family Learning for Item Recommendation and Generation , 2017, AAAI.

[9]  Jon Louis Bentley,et al.  Multidimensional binary search trees used for associative searching , 1975, CACM.

[10]  Derek Bridge,et al.  Product Recommendation for Small-Scale Retailers , 2015, EC-Web.

[11]  Greg Linden,et al.  Two Decades of Recommender Systems at Amazon.com , 2017, IEEE Internet Computing.

[12]  George Karypis,et al.  Feature-based recommendation system , 2005, CIKM '05.

[13]  Bamshad Mobasher,et al.  Controlling Popularity Bias in Learning-to-Rank Recommendation , 2017, RecSys.

[14]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[15]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[16]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[17]  Andrew P. Bradley,et al.  The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..

[18]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[19]  Paul Covington,et al.  Deep Neural Networks for YouTube Recommendations , 2016, RecSys.

[20]  Anton van den Hengel,et al.  Image-Based Recommendations on Styles and Substitutes , 2015, SIGIR.