Author name cluster

Qinglin Jia

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

AAAI Conference 2026 Conference Paper

REACTION: Parameter-Efficient Learning for Recommendation

Song-Li Wu
Zhaocheng Du
Qinglin Jia
Zhenhua Dong

While deep learning (DL) has demonstrated significant success in recommender systems, it suffers from high computational complexity and poor scalability. In this work, we demonstrate, from an information-theoretic perspective, the redundancy of existing DL-based recommender models in two aspects: (1) Feature Redundancy. We show that many features are highly mutually correlated, noisy, or weakly predictive of user-item interaction labels. (2) Structural Redundancy. We further show that a large proportion of parameters in the dense layers contribute minimally to overall performance, indicating significant redundancy within the model architecture. To address these challenges, we propose REACTION (paRameter-Efficient LeArning for recommendaTION), an information-theoretic framework designed to reduce model complexity without sacrificing performance. REACTION consists of two core components: Adaptive Feature Extraction (AFE) leverages mutual information to project high-dimensional sparse features into a compact, informative subspace. This adaptively filters noisy or weak features, reduces embedding parameters, and preserves implicit feature interactions without explicit high-order computation. Dynamic Tower Fusion (DTF) bridges the representational gap between dual-tower expressiveness and single-tower efficiency. It facilitates rich cross-tower interactions during training, then merges the towers into a unified, low-latency single tower for inference. Extensive experiments on four large-scale benchmarks demonstrate that REACTION not only outperforms existing methods in accuracy but also achieves a drastic reduction in both model parameters and inference costs, thus establishing a new paradigm for efficient and scalable recommendation systems.

PDF Details DOI

NeurIPS Conference 2025 Conference Paper

GUI-G1: Understanding R1-Zero-Like Training for Visual Grounding in GUI Agents

Yuqi Zhou
Sunhao Dai
Shuai Wang
Kaiwen Zhou
Qinglin Jia
Jun Xu

Recent Graphical User Interface (GUI) agents replicate the R1-Zero paradigm, coupling online Reinforcement Learning (RL) with explicit chain-of-thought reasoning prior to object grounding and thereby achieving substantial performance gains. In this paper, we first conduct extensive analysis experiments of three key components of that training pipeline: input design, output evaluation, and policy update—each revealing distinct challenges arising from blindly applying general-purpose RL without adapting to GUI grounding tasks. Input design: Current templates encourage the model to generate chain-of-thought reasoning, but longer chains unexpectedly lead to worse grounding performance. Output evaluation: Reward functions based on hit signals or box area allow models to exploit box size, leading to reward hacking and poor localization quality. Policy update: Online RL tends to overfit easy examples due to biases in length and sample difficulty, leading to under-optimization on harder cases. To address these issues, we propose three targeted solutions. First, we adopt a $\textbf{Fast Thinking Template}$ that encourages direct answer generation, reducing excessive reasoning during training. Second, we incorporate a box size constraint into the reward function to mitigate reward hacking. Third, we revise the RL objective by adjusting length normalization and adding a difficulty-aware scaling factor, enabling better optimization on hard samples. Our $\textbf{GUI-G1-3B}$, trained on 17K public samples with Qwen2. 5-VL-3B-Instruct, achieves $\textbf{90. 3\%}$ accuracy on ScreenSpot and $\textbf{37. 1\%}$ on ScreenSpot-Pro. This surpasses all prior models of similar size and even outperforms the larger UI-TARS-7B, establishing a new state-of-the-art in GUI agent grounding.

PDF Details

IJCAI Conference 2021 Conference Paper

UNBERT: User-News Matching BERT for News Recommendation

Qi Zhang
Jingjie Li
Qinglin Jia
Chuyuan Wang
Jieming Zhu
Zhaowei Wang
Xiuqiang He

Nowadays, news recommendation has become a popular channel for users to access news of their interests. How to represent rich textual contents of news and precisely match users' interests and candidate news lies in the core of news recommendation. However, existing recommendation methods merely learn textual representations from in-domain news data, which limits their generalization ability to new news that are common in cold-start scenarios. Meanwhile, many of these methods represent each user by aggregating the historically browsed news into a single vector and then compute the matching score with the candidate news vector, which may lose the low-level matching signals. In this paper, we explore the use of the successful BERT pre-training technique in NLP for news recommendation and propose a BERT-based user-news matching model, called UNBERT. In contrast to existing research, our UNBERT model not only leverages the pre-trained model with rich language knowledge to enhance textual representation, but also captures multi-grained user-news matching signals at both word-level and news-level. Extensive experiments on the Microsoft News Dataset (MIND) demonstrate that our approach constantly outperforms the state-of-the-art methods.

PDF Details DOI