Personalized Ranking on Cascading Behavior Graphs for Accurate Multi-Behavior Recommendation

Multi-behavior recommendation aims to predict the items a user is likely to purchase by analyzing user-item interactions across diverse behaviors. Existing approaches are generally categorized into representation learning (RL) and graph ranking (GR). RL methods generate user and item embeddings in a global manner using graph neural networks and multi-behavioral signals. However, their performance heavily relies on the expressiveness of the learned embeddings, which is often limited by over-smoothing and bias toward frequent interactions. In contrast, GR methods compute user-specific ranking scores over the user-item graph in a query-dependent manner, enabling more direct and fine-grained personalization. Despite this potential, GR has been predominantly studied in single-behavior settings and remains underexplored for multi-behavior recommendation. In this paper, we propose CascadingRank , a novel personalized graph ranking method for multi-behavior recommendation. To achieve better personalization, we first construct a cascading behavior graph that captures the cascading sequence of user behaviors (e.g., viewing, adding to cart, and purchasing). We then compute personalized ranking scores in a staged manner along this graph, where each stage is conditioned on scores of preceding behaviors and query preferences. The model captures behavior-specific influence through cascading stages in graph ranking, yielding more accurate recommendations. To the best of our knowledge, this is the first work to systematically investigate graph ranking as a principled solution for multi-behavior recommendation. Extensive experiments on three real-world datasets show that CascadingRank outperforms state-of-the-art methods, achieving up to 9.56% and 7.16% improvements in HR@10 and NDCG@10, respectively.