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Junhua Fang

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6 papers

1 author row

TIST Journal 2026 Journal Article

Autoregressive STG-based Diffusion Model for Spatiotemporal Trajectory Generation

Tianru Xie
Pingfu Chao
Weizhu Qian
Junhua Fang
Jiajie Xu

The urban foundation model is critical for trajectory-based mobile applications, which require accurate synthesis of paths that adhere to spatial constraints (road networks) and contextual constraints (e.g., weather, traffic). However, existing methods predominantly rely on task-specific models, which fail to holistically capture and integrate diverse spatial patterns (e.g., connectivity) and temporal dynamics (e.g., periodicity, trends) within a cohesive framework, limiting their generalization across diverse prediction tasks. To bridge this gap, we propose AutoDiff, a diffusion-based model generating trajectories on spatial temporal graph (STG), which establishes a new paradigm for trajectory generation as a foundation model for sequential spatiotemporal data. Specifically, we disentangle complex spatiotemporal features as generalizable segment-wise time slices on road networks through autoregressive diffusion generation, which not only enforces realistic trajectory connectivity within road networks, but also enables knowledge transfer across tasks like trajectory recovery and travel time prediction. Besides, we design a confidence-based early-exiting mechanism to eliminate redundant denoising steps without sacrificing quality, enabling scalable applications in mobility analytics. Extensive experiments on three real-world urban trajectory datasets demonstrate the superior performance of AutoDiff in path prediction, trajectory recovery and time estimation tasks, outperforming task-specific baselines while maintaining computational efficiency.

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AAAI Conference 2026 Conference Paper

Wavelet Enhanced Adaptive Frequency Filter for Sequential Recommendation

Huayang Xu
Huanhuan Yuan
Guanfeng Liu
Junhua Fang
Lei Zhao
Pengpeng Zhao

Sequential recommendation has garnered significant attention for its ability to capture dynamic preferences by mining users’ historical interaction data. Given that users’ complex and intertwined periodic preferences are difficult to disentangle in the time domain, recent research is exploring frequency domain analysis to identify these hidden patterns. However, current frequency-domain-based methods suffer from two key limitations: (i) They primarily employ static filters with fixed characteristics, overlooking the personalized nature of behavioral patterns; (ii) While the global discrete Fourier transform excels at modeling long-range dependencies, it can blur non-stationary signals and short-term fluctuations. To overcome these limitations, we propose a novel method called Wavelet Enhanced Adaptive Frequency Filter for Sequential Recommendation (WEARec). Specifically, it consists of two vital modules: dynamic frequency-domain filtering and wavelet feature enhancement. The former is used to dynamically adjust filtering operations based on behavioral sequences to extract personalized global information, and the latter integrates wavelet transform to reconstruct sequences, enhancing blurred non-stationary signals and short-term fluctuations. Finally, these two modules work synergistically to achieve comprehensive performance and efficiency optimization in long sequential recommendation scenarios. Extensive experiments on four widely-used benchmark datasets demonstrate the superiority of WEARec.

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IJCAI Conference 2025 Conference Paper

DASS: A Dual-Branch Attention-based Framework for Trajectory Similarity Learning with Spatial and Semantic Fusion

Jiayi Li
Junhua Fang
Pingfu Chao
Jiajie Xu
Pengpeng Zhao

Trajectory similarity aims to identify pairs of similar trajectories, serving as a crucial operation in spatial-temporal data mining. Although several approaches have been proposed, they encounter the following two issues: 1) An overemphasis on spatial similarity in road networks while the rich semantic information embedded in trajectories is not fully exploited; 2) Dependence on Recurrent Neural Network (RNN) architectures would struggle to capture long-term dependencies. To address these limitations, we propose a Dual-branch Attention-based framework with Spatial and Semantic information (DASS) based on self-supervised learning. Specifically, DASS comprises two core components: 1) A trajectory representation module that models spatial-temporal adjacent relationships in the form of graph and converts semantics into numerical embeddings. 2) A backbone encoder with a co-attention module to independently process two features before they are integrated. Extensive experiments on real-world datasets demonstrate that DASS outperforms state-of-the-art methods, establishing itself as a novel paradigm.

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AAAI Conference 2025 Conference Paper

Fuzzy Collaborative Reasoning

Huanhuan Yuan
Pengpeng Zhao
Jiaqing Fan
Junhua Fang
Guanfeng Liu
Victor S. Sheng

Collaborative reasoning enhances recommendation performance by combining the strengths of symbolic learning and deep neural learning. However, current collaborative reasoning models rely on parameterized networks to simulate logical operations within the reasoning process, which (1) do not comply with all axiomatic principles of classical logic and (2) limit the model's generalizability. To address these limitations, a Fuzzy logic approach tailored for Collaborative Reasoning (FuzzCR) is proposed in this work, aiming to augment the recommendation system with cognitive abilities. Specifically, this method redefines the sequential recommendation task as a logical query answering process to facilitate a more structured and logical progression of reasoning. Moreover, learning-free fuzzy logical operations are implemented for the designed reasoning process. Taking advantage of the inherent properties of fuzzy logic, these logical operations satisfy fundamental logical rules and ensure complete reasoning. After training, these operations can be applied to flexible reasoning processes, rather than being confined to fixed computation graphs, thereby exhibiting good generalizability. Extensive experiments conducted on publicly available datasets demonstrate the superiority of this method in solving the sequential recommendation task.

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IJCAI Conference 2020 Conference Paper

Collaborative Self-Attention Network for Session-based Recommendation

Anjing Luo
Pengpeng Zhao
Yanchi Liu
Fuzhen Zhuang
Deqing Wang
Jiajie Xu
Junhua Fang
Victor S. Sheng

Session-based recommendation becomes a research hotspot for its ability to make recommendations for anonymous users. However, existing session-based methods have the following limitations: (1) They either lack the capability to learn complex dependencies or focus mostly on the current session without explicitly considering collaborative information. (2) They assume that the representation of an item is static and fixed for all users at each time step. We argue that even the same item can be represented differently for different users at the same time step. To this end, we propose a novel solution, Collaborative Self-Attention Network (CoSAN) for session-based recommendation, to learn the session representation and predict the intent of the current session by investigating neighborhood sessions. Specially, we first devise a collaborative item representation by aggregating the embedding of neighborhood sessions retrieved according to each item in the current session. Then, we apply self-attention to learn long-range dependencies between collaborative items and generate collaborative session representation. Finally, each session is represented by concatenating the collaborative session representation and the embedding of the current session. Extensive experiments on two real-world datasets show that CoSAN constantly outperforms state-of-the-art methods.

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IJCAI Conference 2019 Conference Paper

Graph Contextualized Self-Attention Network for Session-based Recommendation

Chengfeng Xu
Pengpeng Zhao
Yanchi Liu
Victor S. Sheng
Jiajie Xu
Fuzhen Zhuang
Junhua Fang
Xiaofang Zhou

Session-based recommendation, which aims to predict the user's immediate next action based on anonymous sessions, is a key task in many online services (e. g. , e-commerce, media streaming). Recently, Self-Attention Network (SAN) has achieved significant success in various sequence modeling tasks without using either recurrent or convolutional network. However, SAN lacks local dependencies that exist over adjacent items and limits its capacity for learning contextualized representations of items in sequences. In this paper, we propose a graph contextualized self-attention model (GC-SAN), which utilizes both graph neural network and self-attention mechanism, for session-based recommendation. In GC-SAN, we dynamically construct a graph structure for session sequences and capture rich local dependencies via graph neural network (GNN). Then each session learns long-range dependencies by applying the self-attention mechanism. Finally, each session is represented as a linear combination of the global preference and the current interest of that session. Extensive experiments on two real-world datasets show that GC-SAN outperforms state-of-the-art methods consistently.

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