Modeling Customers and Products with Word Embeddings from Receipt Data

For many tasks in market research it is important to model customers and products as comparable instances. Usually, the integration of customers and products into one model is not trivial. In this paper, we will detail an approach for a combined vector space of customers and products based on word embeddings learned from receipt data. To highlight the strengths of this approach we propose four different applications: recommender systems, customer and product segmentation and purchase prediction. Experimental results on a real-world dataset with 200M order receipts for 2M customers show that our word embedding approach is promising and helps to improve the quality in these applications scenarios.

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