In recent years, Large Language Models (LLMs) have gained increasing attention in personalized recommendation systems due to their strong capabilities to understand contextual and semantic information. However, most existing LLM-based recommendation methods primarily rely on textual features, often neglecting the unique structural information embedded in user and item IDs, which are critical for capturing personalized preferences. To bridge this gap, we propose a novel method called CL 3M - Rec, which enhances the semantic representation of user and item IDs through Contrastive Learning (CL) and optimizes LLM recommendation performance via prompt construction. By effectively integrating structured ID information with textual semantics, our approach enables the LLMs to better model individual user interests and improve personalized prediction. Experiments on four public datasets demonstrate that CL3M-Rec consistently outperforms existing LLM-based and CL-based recommendation approaches, achieving superior predictive accuracy and robust generalization across diverse recommendation scenarios. The code of our method is available at https://github.com/Lewis44zhou/CL-3M-Rec.

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.