1 citations · 2 across the 12 of their papers we have counts for
7 papers · 1 filter
IDProxy: CTR Prediction with Multimodal LLMs for Cold-Start Recommendation at Xiaohongshu
Yubin Zhang, Haiming Xu, Guillaume Salha-Galvan +6
Content-driven platforms such as Xiaohongshu often leverage click-through rate (CTR) prediction models for recommendation. However, these models depend heavily on item ID embedding…
SAGE: Sequence-level Adaptive Gradient Evolution for Generative Recommendation
Yu Xie, Xing Kai Ren, Ying Qi +1
Reinforcement learning-based preference optimization is increasingly used to align list-wise generative recommenders with complex, multi-objective user feedback, yet existing optim…
LASER: An Efficient Target-Aware Segmented Attention Framework for End-to-End Long Sequence Modeling
Tianhe Lin, Ziwei Xiong, Baoyuan Ou +8
Modeling ultra-long user behavior sequences is pivotal for capturing evolving and lifelong interests in modern recommendation systems. However, deploying such models in real-time i…
QP-OneModel: A Unified Generative LLM for Multi-Task Query Understanding in Xiaohongshu Search
Jianzhao Huang, Xiaorui Huang, Fei Zhao +9
Query Processing (QP) bridges user intent and content supply in large-scale Social Network Service (SNS) search engines. Traditional QP systems rely on pipelines of isolated discri…
Guiding the Recommender: Information-Aware Auto-Bidding for Content Promotion
Yumou Liu, Zhenzhe Zheng, Jiang Rong +3
Modern content platforms offer paid promotion to mitigate cold start by allocating exposure via auctions. Our empirical analysis reveals a counterintuitive flaw in this paradigm: w…
Benchmark^2: Systematic Evaluation of LLM Benchmarks
Qi Qian, Chengsong Huang, Jingwen Xu +13
The rapid proliferation of benchmarks for evaluating large language models (LLMs) has created an urgent need for systematic methods to assess benchmark quality itself. We propose B…
Robust Tool Use via Fission-GRPO: Learning to Recover from Execution Errors
Zhiwei Zhang, Fei Zhao, Rui Wang +6
Large language models (LLMs) can call tools effectively, yet they remain brittle in multi-turn execution: after a tool-call error, smaller models often fall into repetitive invalid…