A Neural Bag-of-Words Point Process Model for User Return Time Prediction in E-commerce

Monitoring and predicting user engagement is essential to gauge the overall health of an E-commerce platform. A healthy active user base indicates that the platform is able to retain users and is performing well on the user satisfaction metric. To measure long-term user satisfaction, predicting the return rate of a user is essential. The frequent return of the user indicates that they are overall satisfied with the platform. To this end, we consider the problem of predicting users' return time on the platform given their historical interactions. The current state-of-the-art models for user return time prediction are based on recurrent neural network, which models the sequence of user interactions and predicts the return time using a Temporal Point Process based formulation. However, it is well-known that the inference time for these models grows as the sequence length increases, due to the complex recurrent and gating mechanisms, which deems them unfit to be used in a real-time prediction setting. Towards this end, we propose a lightweight and simple neural bag-of-words-based model for user return time prediction, which considers the user activity trail as a bag-of-words embedding model and performs a simple aggregation operation, used for the final prediction. We perform experiments on interaction log data from a major e-commerce company, and our proposed bag-of-words model outperforms the complex recurrence-based neural network by 6.14% and 4.81% on average, in terms of the Root-Mean-Squared-Error and Mean-Absolute-Error, respectively. We also compare the inference time of our method to the recurrent neural network-based method, with an overall reduction of 78.5% in terms of the wall-clock time.