Towards Preprocessing Guidelines for Neural Network Embedding of Customer Behavior in Digital Retail

Shopping transactions in digital retailing platforms enable retailers to understand customers' needs for providing personalized experiences. Researchers started modeling transaction data through neural network embedding, which enables unsupervised learning of contextual similarities between attributes in shopping transactions. However, every study brings different approaches for embedding customer's transactions, and clear preprocessing guidelines are missing. This paper reviews the recent literature of neural embedding for customer behavior and brings three main contributions. First, we provide a set of guidelines for preprocessing and modeling consumer transaction data to learn neural network embeddings. Second, it is introduced a multi-task Long Short-Term Memory Network to evaluate the guidelines proposed through the task of purchase behavior prediction. Third, we present a multi-contextual visualization of customer behavior embeddings, and its usefulness for purchase prediction and fraud detection applications. Results achieved illustrate accuracies above 40%, 60%, and 80% for predicting the next days, hours, and products purchased for some customers in a dataset composed of online grocery shopping transactions.

[1]  Sonia San Martín Gutiérrez,et al.  Tell me what they are like and I will tell you where they buy. An analysis of omnichannel consumer behavior , 2017, Comput. Hum. Behav..

[2]  Ji-Rong Wen,et al.  Taxonomy-Aware Multi-Hop Reasoning Networks for Sequential Recommendation , 2019, WSDM.

[3]  Yongfeng Zhang,et al.  Sequential Recommendation with User Memory Networks , 2018, WSDM.

[4]  Ankur Datta,et al.  Predicting Shopping Behavior with Mixture of RNNs , 2017, eCOM@SIGIR.

[5]  Eric T. Bradlow,et al.  The Role of Big Data and Predictive Analytics in Retailing , 2017 .

[6]  Rebeca San Jos Cabezudo,et al.  Tell me what they are like and I will tell you where they buy. An analysis of omnichannel consumer behavior , 2017 .

[7]  Shahryar Rahnamayan,et al.  Customer shopping pattern prediction: A recurrent neural network approach , 2016, 2016 IEEE Symposium Series on Computational Intelligence (SSCI).

[8]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[9]  Wolfgang Lehner,et al.  Modeling Customers and Products with Word Embeddings from Receipt Data , 2018, IDEAS.

[10]  Tao Xiang,et al.  Learning a Deep Embedding Model for Zero-Shot Learning , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[12]  Cheng Guo,et al.  Entity Embeddings of Categorical Variables , 2016, ArXiv.

[13]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[14]  Oren Barkan,et al.  ITEM2VEC: Neural item embedding for collaborative filtering , 2016, 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP).

[15]  Michael Granitzer,et al.  Sequence classification for credit-card fraud detection , 2018, Expert Syst. Appl..

[16]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[17]  Sunil Erevelles,et al.  Big Data consumer analytics and the transformation of marketing , 2016 .

[18]  Yusuf Sinan Akgül,et al.  Continuous Embedding Spaces for Bank Transaction Data , 2017, ISMIS.

[19]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[20]  Roland Vollgraf,et al.  An LSTM-Based Dynamic Customer Model for Fashion Recommendation , 2017, RecTemp@RecSys.

[21]  Tao Li,et al.  Attention-based recurrent neural network for location recommendation , 2017, 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE).

[22]  Matthias Rettenmeier,et al.  Understanding Consumer Behavior with Recurrent Neural Networks , 2017 .

[23]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[24]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[25]  Ronan G. Reilly,et al.  Predicting Purchasing Intent: Automatic Feature Learning using Recurrent Neural Networks , 2018, eCOM@SIGIR.