From the 1 of 10 linked papers with an AI index.
1 citations · 1 across the 4 of their papers we have counts for
10 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
The paper introduces SWAG-Bid, a hierarchical auto‑bidding system that plans and executes bids while accounting for sliding‑window efficiency constraints over multiple days, using…
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…
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…
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…
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…