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SADRec: Semantic-aligned adversarial diffusion model for multi-behavior recommendation
Xian Mo1, Zhiqi Wang1, Rui Tang2
1School of Information Engineering, Ningxia University, Yinchuan, 750021, China.
Summary
This study introduces a novel Semantic-aligned Adversarial Diffusion Model (SADRec) for multi-behavior recommendation. SADRec enhances recommendation accuracy by precisely aligning auxiliary behaviors with target behaviors, effectively addressing data sparsity.
Area of Science:
- Computer Science
- Artificial Intelligence
- Machine Learning
Background:
- Multi-behavior recommendation systems utilize auxiliary behaviors to infer valuable target behaviors.
- Current methods struggle with fine-grained alignment between target and auxiliary behaviors, limiting recommendation precision.
Purpose of the Study:
- To propose a novel Semantic-aligned Adversarial Diffusion Model (SADRec) for multi-behavior recommendation.
- To enhance the alignment mechanism between target and auxiliary behaviors for improved recommendation accuracy.
Main Methods:
- Developed an Adversarial Diffusion Model (ADM) using the target behavior as a discriminative anchor for guided reconstruction.
- Implemented a Multi-level Negative Sampling (MNS) mechanism with graph contrastive learning for optimized alignment.
- Achieved fine-grained semantic calibration by adaptively identifying key semantic dimensions and preserving discrepancies.
Main Results:
- SADRec demonstrated superior performance compared to state-of-the-art baselines across three public datasets.
- The proposed model effectively alleviates the challenge of data sparsity in recommendation tasks.
- Experimental results validate the efficacy of fine-grained semantic calibration and multi-level negative sampling.
Conclusions:
- SADRec offers a significant advancement in multi-behavior recommendation by enabling precise semantic alignment.
- The model's ability to handle data sparsity makes it a valuable tool for real-world recommendation scenarios.
- The proposed approach provides a robust framework for leveraging auxiliary behaviors in recommendation systems.
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