Periodic Prompt on Dynamic Heterogeneous Graph for Next Basket Recommendation

In next basket recommendation, baskets are usually formed through a large number of user interactions with items in the early stage. In general, the existing methods for next basket recommendation primarily focus on historical purchase behavior of users, assuming that user purchase interests are static, and overlook the dynamic and diverse changes in user purchase interests. In order to fully capture dynamic user interests and provide users with more diverse recommendations, we propose our method, Dynamic Heterogeneous Graph Prompt (DHGP), for next basket recommendation. By constructing a dynamic heterogeneous graph, we can adequately consider the influence of various interactive behaviors on the user's baskets at different times. Furthermore, we introduce a periodic dynamic heterogeneous prompt strategy to capture the interest directions between baskets from different users and provide users with more diverse interest directions. Extensive experimental validation on six real world datasets demonstrates that our method shows strong applicability across datasets under various conditions and outperforms several state-of-the-art recommendation methods. To the best of our knowledge, DHGP is the first next basket recommendation method that effectively combines dynamic and heterogeneous information. The implementation code is accessible at https://github.com/AllminerLab.

I. Introduction

The next basket recommendation task is a common real-world scenario that aims to predict the items a user will interact with in the next basket based on historical user-item interaction data. Unlike traditional recommendation tasks, the next basket recommendation does not consider the temporal order of items within a basket, and the items within a basket are typically interrelated to some extent. Overall, a common challenge in recommendation systems is how to well capture the direction of user interests over time.