In this paper, we focus on how to fully capture the potential item relation within and across sequences. Specifically, we propose a novel graph collaborative ...

This paper proposes a novel graph collaborative optimization-based method named GOSR, which not only comprehensively explores the real item relation from ...

The proposed approach here is composed of several algorithms, called the ARSCCOrg (Algorithms for RSCCOrg) and can better cope with inter-organizational ...

Mar 15, 2024 · As illustrated in Fig. 2, our framework involves three key modules: linear graph propagation module, dual gating block, and model prediction.

This paper addresses the challenge of modeling sequential graphs and utilizes both collaborative and sequential information to make recommendations. To this ...

Dec 5, 2024 · Graph-based and sequential methods are two popular recommendation paradigms, each excelling in its domain but lacking the ability to leverage ...

Feb 15, 2022 · In this paper, we propose Graph Collaborative Reasoning (GCR), which can use the neighbor link information for relational reasoning on graphs from logical ...

Jul 27, 2021 · We take inspiration from dynamic graph neural networks to cope with this challenge, modeling the user sequence and dynamic collaborative signals ...

This paper proposes a novel recommendation framework, namely Graph Contrastive Learning for. Sequential Recommendation (GCL4SR). Specifi- cally, GCL4SR employs ...

Understanding collaborative and sequential information plays a major role in next-item recommendation. Although previous models have achieved considerable ...