1 citations · 1 across the 12 of their papers we have counts for
13 papers
Reasoning Quality Matters: Combating Reasoning Collapse in LLM-based Embedding Learning
Zihan Gong, Xiaohan Ye, Jiangchao Yao +3
Large Language Models (LLMs) have recently shown strong potential for producing context-rich text embeddings for retrieval. Most existing methods either treat embedding learning as…
Structural Process Supervision for Latent Chain-of-Thought Reasoning
Yiqi Li, Xu Chen, Chen Ju +6
Latent reasoning approaches enhance token-level efficiency and robustness by replacing verbose, explicit chain-of-thought (CoT) tokens with compact continuous-space embeddings. How…
Pailitao-MMSearch: Building Native E-Commerce Multimodal Search Foundation
Xiaohan Ye, Xu Chen, Zihan Gong +15
The evolution of e-commerce has fundamentally transformed how users search for products, shifting from simple text-based keyword queries to complex multimodal interactions that sea…
Pailitao-VL: Unified Embedding and Reranker for Real-Time Multi-Modal Industrial Search
Lei Chen, Chen Ju, Xu Chen +13
In this work, we presented Pailitao-VL, a comprehensive multi-modal retrieval system engineered for high-precision, real-time industrial search. We here address three critical chal…
Enhancing Sequential Recommendation with World Knowledge from Large Language Models
Tianjie Dai, Xu Chen, Yunmeng Shu +4
Sequential Recommendation System~(SRS) has become pivotal in modern society, which predicts subsequent actions based on the user's historical behavior. However, traditional collabo…
Explore More, Learn Better: Parallel MLLM Embeddings under Mutual Information Minimization
Zhicheng Wang, Chen Ju, Xu Chen +5
Embedding models are a cornerstone of modern AI. Driven by Multimodal Large Language Models (MLLMs), they have made great progress in architecture and data curation, while the holi…
Wave-Particle (Continuous-Discrete) Dualistic Visual Tokenization for Unified Understanding and Generation
Yizhu Chen, Chen Ju, Zhicheng Wang +5
The unification of understanding and generation within a single multi-modal large model (MLLM) remains one significant challenge, largely due to the dichotomy between continuous an…
Leveraging Scene Context with Dual Networks for Sequential User Behavior Modeling
Xu Chen, Yunmeng Shu, Yuangang Pan +6
Modeling sequential user behaviors for future behavior prediction is crucial in improving user's information retrieval experience. Recent studies highlight the importance of incorp…
Counterfactual Learning-Driven Representation Disentanglement for Search-Enhanced Recommendation
Jiajun Cui, Xu Chen, Shuai Xiao +4
For recommender systems in internet platforms, search activities provide additional insights into user interest through query-click interactions with items, and are thus widely use…
Learning Multi-Branch Cooperation for Enhanced Click-Through Rate Prediction at Taobao
Xu Chen, Zida Cheng, Yuangang Pan +6
Existing click-through rate (CTR) prediction works have studied the role of feature interaction through a variety of techniques. Each interaction technique exhibits its own strengt…
Turbo: Informativity-Driven Acceleration Plug-In for Vision-Language Large Models
Chen Ju, Haicheng Wang, Haozhe Cheng +6
Vision-Language Large Models (VLMs) recently become primary backbone of AI, due to the impressive performance. However, their expensive computation costs, i.e., throughput and dela…
Turbo: Informativity-Driven Acceleration Plug-In for Vision-Language Models
Chen Ju, Haicheng Wang, Zeqian Li +4
Vision-Language Large Models (VLMs) have become primary backbone of AI, due to the impressive performance. However, their expensive computation costs, i.e., throughput and delay, i…
Enhancing Cross-domain Click-Through Rate Prediction via Explicit Feature Augmentation
Xu Chen, Zida Cheng, Jiangchao Yao +5
Cross-domain CTR (CDCTR) prediction is an important research topic that studies how to leverage meaningful data from a related domain to help CTR prediction in target domain. Most…