24 citations · 33 across the 17 of their papers we have counts for
5 papers · 1 filter
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…
Knowledge Enhanced Multi-intent Transformer Network for Recommendation
Ding Zou, Wei Wei, Feida Zhu +3
Incorporating Knowledge Graphs into Recommendation has attracted growing attention in industry, due to the great potential of KG in providing abundant supplementary information and…
Search Intenion Network for Personalized Query Auto-Completion in E-Commerce
Wei Bao, Mi Zhang, Tao Zhang +1
Query Auto-Completion(QAC), as an important part of the modern search engine, plays a key role in complementing user queries and helping them refine their search intentions.Today's…
Gaussian Graph with Prototypical Contrastive Learning in E-Commerce Bundle Recommendation
Zhao-Yang Liu, Liucheng Sun, Chenwei Weng +2
Bundle recommendation aims to provide a bundle of items to satisfy the user preference on e-commerce platform. Existing successful solutions are based on the contrastive graph lear…
Trustworthy Knowledge Graph Completion Based on Multi-sourced Noisy Data
Jiacheng Huang, Yao Zhao, Wei Hu +5
Knowledge graphs (KGs) have become a valuable asset for many AI applications. Although some KGs contain plenty of facts, they are widely acknowledged as incomplete. To address this…