TEE: Real-Time Purchase Prediction Using Time Extended: Embeddings for Representing Customer Behavior

• Miguel Alves Gomes ^[1]
  1. [1] University of Wuppertal

     University of Wuppertal

     Kreisfreie Stadt Wuppertal, Alemania

     
• Localización: Journal of Theoretical and Applied Electronic Commerce Research, ISSN-e 0718-1876, Vol. 18, Nº. 3, 2023, págs. 1404-1418
• Idioma: inglés
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• Resumen

  • Real-time customer purchase prediction tries to predict which products a customer will buy next. Depending on the approach used, this involves using data such as the customer’s past purchases, his or her search queries, the time spent on a product page, the customer’s age and gender, and other demographic information. These predictions are then used to generate personalized recommendations and offers for the customer. A variety of approaches already exist for real-time customer purchase prediction. However, these typically require expertise to create customer representations.

    Recently, embedding-based approaches have shown that customer representations can be effectively learned. In this regard, however, the current state-of-the-art does not consider activity time. In this work, we propose an extended embedding approach to represent the customer behavior of a session for both known and unknown customers by including the activity time. We train a long short-term memory with our representation. We show with empirical experiments on three different real-world datasets that encoding activity time into the embedding increases the performance of the prediction and outperforms the current approaches used.