2 citations · 3 across the 10 of their papers we have counts for
13 papers
UniSGR-V2: Stepwise Target-Side Feature Anticipation and Crossing for Generative Recommendation
Jun Yang, Jiasi Chen, Jiawei Sun +4
Semantic-ID-based generative recommendation unifies retrieval and ranking through autoregressive decoding but differs structurally from discriminative models, which can directly ac…
LazFormer: Scaling Transformers for Industrial Recommendation via Transferable Generative Pre-training
Xiaodong Li, Alin Fan, Mingyang Li +7
Transformers have shown promising performance in LLMs due to their outstanding scalability, several studies have investigated the scalability of Transformers for industrial recomme…
MIMA: Multi-Interest Recommendation via Multi-Positive Exclusive Assignment
Xingyuan Mao, Alin Fan, Shichao Nie +4
Multi-interest recommendation represents each user with multiple interest vectors for fine-grained candidate matching, yet it often suffers from interest collapse, where the learne…
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…
FlowHijack: A Dynamics-Aware Backdoor Attack on Flow-Matching Vision-Language-Action Models
Xinyuan An, Tao Luo, Gengyun Peng +3
Vision-Language-Action (VLA) models are emerging as a cornerstone for robotics, with flow-matching policies like showing great promise in generating smooth, continuous action…
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…
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…
Convergence Guarantees for Gradient-Based Training of Neural PDE Solvers: From Linear to Nonlinear PDEs
Wei Zhao, Tao Luo
We present a unified convergence theory for gradient-based training of neural network methods for partial differential equations (PDEs), covering both physics-informed neural netwo…
Analyzing and Bridging the Gap between Maximizing Total Reward and Discounted Reward in Deep Reinforcement Learning
Shuyu Yin, Fei Wen, Peilin Liu +1
The optimal objective is a fundamental aspect of reinforcement learning (RL), as it determines how policies are evaluated and optimized. While total return maximization is the idea…
Probing Implicit Bias in Semi-gradient Q-learning: Visualizing the Effective Loss Landscapes via the Fokker--Planck Equation
Shuyu Yin, Fei Wen, Peilin Liu +1
Semi-gradient Q-learning is applied in many fields, but due to the absence of an explicit loss function, studying its dynamics and implicit bias in the parameter space is challengi…
Decision Mamba: Reinforcement Learning via Hybrid Selective Sequence Modeling
Sili Huang, Jifeng Hu, Zhejian Yang +5
Recent works have shown the remarkable superiority of transformer models in reinforcement learning (RL), where the decision-making problem is formulated as sequential generation. T…