Personalized Behavior Prediction with Encoder-to-Decoder Structure

Abstract

With the rise of the Internet industry and the technique of artificial intelligence, personalized services are increasingly important in recent years for improving user experience and increasing corporates' competitiveness and profits. Precise prediction of customers' behaviors has shown great effects in modern business marketing, especially when making personalized decisions. In this paper, we develop a deep learning network to make personalized predictions of their behaviors among a list of potential choices. The architecture of this model combines each user's features and his historical event lists by sequence-to-sequence (Seq2Seq) structure and make predictions based on his recent event lists. We also modify the long-short- term memory (LSTM) cell forget gate's structure to enhance the attention ability. Such design, called the attetioned LSTM, converges quicker and better while still maintain the similar performance in open dataset IMDB. In addition, in dealing with personalized prediction problems in real-world datasets provided by our cooperative company, our attentioned LSTM achieves a 10% higher precision in average than the standard LSTM model. The advantage is confirmed in evaluation of this generic method on a real dataset of users' behaviors sequences and individuals' attribute profiles from Ant Financial. It also achieves a great result working on the real-world business scene. This model can also achieve a great performance working on the real-world business scene.