Publications (62)
Towards Hybrid-grained Feature Interaction Selection for Deep Sparse Network
Fuyuan Lyu, Xing Tang, Dugang Liu +5
Deep sparse networks are widely investigated as a neural network architecture for prediction tasks with high-dimensional sparse features, with which feature interaction selection i…
Self-Sampling Training and Evaluation for the Accuracy-Bias Tradeoff in Recommendation
Dugang Liu, Yang Qiao, Xing Tang +4
Research on debiased recommendation has shown promising results. However, some issues still need to be handled for its application in industrial recommendation. For example, most o…
Efficient Long-Sequence Diffusion Modeling for Symbolic Music Generation
Jinhan Xu, Xing Tang, Houpeng Yang +7
Symbolic music generation is a challenging task in multimedia generation, involving long sequences with hierarchical temporal structures, long-range dependencies, and fine-grained…
Towards Automated Negative Sampling in Implicit Recommendation
Fuyuan Lyu, Yaochen Hu, Xing Tang +3
Negative sampling methods are vital in implicit recommendation models as they allow us to obtain negative instances from massive unlabeled data. Most existing approaches focus on s…
BoRA: Towards More Expressive Low-Rank Adaptation with Block Diversity
Shiwei Li, Xiandi Luo, Haozhao Wang +6
Low-rank adaptation (LoRA) is a parameter-efficient fine-tuning (PEFT) method widely used in large language models (LLMs). It approximates the update of a pretrained weight matrix…
RecDCL: Dual Contrastive Learning for Recommendation
Dan Zhang, Yangliao Geng, Wenwen Gong +6
Self-supervised learning (SSL) has recently achieved great success in mining the user-item interactions for collaborative filtering. As a major paradigm, contrastive learning (CL)…