4 citations · 5 across the 12 of their papers we have counts for
12 papers
GeoReward: Mitigating Contextual Variable Overestimation in Vision-Language Models for Cross-Market Preference Prediction
Shuo Liu, Huixiang Cai, Weiru Zhang +1
Vision-language models excel in many multimodal tasks but remain prone to a subtle yet impactful failure mode: they tend to overestimate dominant visual-textual cues while underest…
Beyond Single-Episode Optimization: Sliding-Window Aware Generative Auto-Bidding for Long-Term Advertising Effectiveness
Binglin Wu, Chuan Yue, Yingyi Zhang +4
Auto-bidding systems optimize bids to maximize value under efficiency constraints such as Cost-Per-Action (CPA). Existing methods treat each day as an independent episode. However,…
QueryAgent-R1: Bridging Query Generation and Product Retrieval for E-Commerce Query Recommendation
Dike Sun, Zheng Zou, Jingtong Zang +3
Query recommendation in e-commerce search aims to proactively suggest queries that match users' potential interests. However, existing methods mainly optimize query-level relevance…
Quality Over Clicks: Iterative Reinforcement Learning for Early-Stage E-Commerce Query Suggestion
Qi Sun, Kejun Xiao, Huaipeng Zhao +2
Existing dialogue systems rely on query suggestion to enhance user engagement. Recent approaches mainly optimize generative models using click-through rate (CTR) models to align wi…
Shopping Companion: Benchmarking and Training LLM Agents for Long-Horizon Preference-Grounded E-Commerce Tasks
Zijian Yu, Kejun Xiao, Huaipeng Zhao +2
In e-commerce, LLM agents show promise for shopping tasks such as recommendations, budget management, and bundle deals, where accurately capturing user preferences from long-horizo…
ProductResearch: Training E-Commerce Deep Research Agents via Multi-Agent Synthetic Trajectory Distillation
Jiangyuan Wang, Kejun Xiao, Huaipeng Zhao +2
Large Language Model (LLM)-based agents show promise for e-commerce conversational shopping, yet existing implementations lack the interaction depth and contextual breadth required…
Learning to Reflect and Correct: Towards Better Decoding Trajectories for Large-Scale Generative Recommendation
Haibo Xing, Hao Deng, Lingyu Mu +4
Generative Recommendation (GR) has become a promising paradigm for large-scale recommendation systems. However, existing GR models typically perform single-pass decoding without ex…
PRO-Bid: Pareto-Prioritized Regret Optimization for Constraint-Aware Generative Auto-Bidding
Binglin Wu, Yingyi Zhang, Xianneng Li +4
Auto-bidding systems strive to maximize marketing value while maintaining high compliance with efficiency constraints, such as Target Cost-Per-Action (CPA). While Decision Transfor…
Thinking Broad, Acting Fast: Latent Reasoning Distillation from Multi-Perspective Chain-of-Thought for E-Commerce Relevance
Baopu Qiu, Hao Chen, Yuanrong Wu +4
Effective relevance modeling is crucial for e-commerce search, as it aligns search results with user intent and enhances customer experience. Recent work has leveraged large langua…
ShoppingBench: A Real-World Intent-Grounded Shopping Benchmark for LLM-based Agents
Jiangyuan Wang, Kejun Xiao, Qi Sun +4
Existing benchmarks in e-commerce primarily focus on basic user intents, such as finding or purchasing products. However, real-world users often pursue more complex goals, such as…
Large Language Model based Long-tail Query Rewriting in Taobao Search
Wenjun Peng, Guiyang Li, Yue Jiang +6
In the realm of e-commerce search, the significance of semantic matching cannot be overstated, as it directly impacts both user experience and company revenue. Along this line, que…
COURIER: Contrastive User Intention Reconstruction for Large-Scale Visual Recommendation
Jia-Qi Yang, Chenglei Dai, Dan OU +5
With the advancement of multimedia internet, the impact of visual characteristics on the decision of users to click or not within the online retail industry is increasingly signifi…