5 papers
Learning from What You Retrieve: Online RL Fine-Tuning for Semantic Retrieval
Shaowei Wei, Chong Huang, Songtao Fang +3
In large-scale e-commerce retrieval, dual-encoder retrievers are op- timized for contrastive similarity, whereas downstream rerankers capture finer-grained relevance preferences; t…
Generative Retrieval for E-commerce: Jointly Learning Embedding and Codebook with Same Product Cluster
Songtao Fang, Zihao Xu, Shaowei Wei +2
With the development of large language models (LLMs), generative retrieval is becoming increasingly important in e-commerce scenarios. Current mainstream approaches typically use a…
Learning Subspace-Preserving Sparse Attention Graphs from Heterogeneous Multiview Data
Jie Chen, Yuanbiao Gou, Chuanbin Liu +2
The high-dimensional features extracted from large-scale unlabeled data via various pretrained models with diverse architectures are referred to as heterogeneous multiview data. Mo…
Multiview Self-Representation Learning across Heterogeneous Views
Jie Chen, Zhu Wang, Chuanbin Liu +1
Features of the same sample generated by different pretrained models often exhibit inherently distinct feature distributions because of discrepancies in the model pretraining objec…
Conditional Distribution Learning for Graph Classification
Jie Chen, Hua Mao, Chuanbin Liu +2
Leveraging the diversity and quantity of data provided by various graph-structured data augmentations while preserving intrinsic semantic information is challenging. Additionally,…