Personalized Ranking on Cascading Behavior Graphs for Accurate Multi-Behavior Recommendation
Geonwoo Ko, Minseo Jeon, Jinhong Jung

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…
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Taxonomy
TopicsMental Health Research Topics · Opinion Dynamics and Social Influence
