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

TL;DR

This paper introduces CascadingRank, a graph ranking method that models sequential user behaviors for improved multi-behavior recommendation accuracy, outperforming existing methods.

Contribution

It proposes a novel cascading behavior graph and an iterative ranking algorithm tailored for multi-behavior recommendation, addressing limitations of prior approaches.

Findings

CascadingRank achieves up to 9.56% improvement in HR@10.

It outperforms state-of-the-art methods in NDCG@10.

Theoretical analysis confirms its effectiveness, convergence, and scalability.

Abstract

Multi-behavior recommendation predicts items a user may purchase by analyzing diverse behaviors like viewing, adding to a cart, and purchasing. Existing methods fall into two categories: representation learning and graph ranking. Representation learning generates user and item embeddings to capture latent interaction patterns, leveraging multi-behavior properties for better generalization. However, these methods often suffer from over-smoothing and bias toward frequent interactions, limiting their expressiveness. Graph ranking methods, on the other hand, directly compute personalized ranking scores, capturing user preferences more effectively. Despite their potential, graph ranking approaches have been primarily explored in single-behavior settings and remain underutilized for multi-behavior recommendation. In this paper, we propose CascadingRank, a novel graph ranking method for multi-behavior recommendation. It models the natural sequence of user behaviors (e.g., viewing, adding to cart, and purchasing) through a cascading behavior graph. An iterative algorithm computes ranking scores, ensuring smoothness, query fitting, and cascading alignment. Experiments on three real-world datasets demonstrate that CascadingRank outperforms state-of-the-art methods, with up to 9.56% and 7.16% improvements in HR@10 and NDCG@10, respectively. Furthermore, we provide theoretical analysis highlighting its effectiveness, convergence, and scalability, showcasing the advantages of graph ranking in multi-behavior recommendation.