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Jingjing Gu

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10 papers

2 author rows

AAAI Conference 2026 Conference Paper

Exploiting Pre-trained Language Model for Cross-city Urban Flow Prediction Guided by Information-theoretic Analysis

Qiang Zhou
Xudong Tong
Yuting Liu
ChuanXing Liu
Jingjing Gu

Cross-city urban flow prediction is critical for democratizing smart application benefits in data-scarce developing cities. However, existing methods face an inherent performance ceiling, constrained by both the inevitably finite samples from the source city and the distributional gap between cities. In this paper, we present PLM-CUP, the first theoretically-grounded framework that breaks this bottleneck by leveraging a pre-trained language model (PLM) as an additional source domain. Through an information-theoretic analysis of the generalization error bound, we reveal that the key challenge lies in constructing a semantic bridge encoder and a task-specific adapter to enable cross-domain alignment when incorporating a PLM. Accordingly, PLM-CUP adopts a three-stage architecture, including a semantic bridge encoder that transforms spatiotemporal flow patterns into languagealigned representations via trend-periodicity decomposition, a PLM fine-tuned for knowledge transfer, and a task adapter with spatiotemporal self-attention to conduct multi-step prediction. We further introduce GDAConv, a graph convolution module with dual activation functions that enhances spatial modeling throughout the framework. Experiments on real-world datasets demonstrate that PLM-CUP significantly outperforms state-of-the-art baselines, validating the effectiveness of the proposed PLM enhanced cross-city transfer paradigm for urban flow prediction.

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ICML Conference 2025 Conference Paper

Janus: Dual-Server Multi-Round Secure Aggregation with Verifiability for Federated Learning

Lang Pu
Jingjing Gu
Chao Lin 0003
Xinyi Huang 0001

Secure Aggregation (SA) is a cornerstone of Federated Learning (FL), ensuring that user updates remain hidden from servers. The advanced Flamingo (S&P’23) has realized multi-round aggregation and improved efficiency. However, it still faces several key challenges: scalability issues with dynamic user participation, a lack of verifiability for server-side aggregation results, and vulnerability to Model Inconsistency Attacks (MIA) caused by a malicious server distributing inconsistent models. To address these issues, we propose $\textit{Janus}$, a generic SA scheme based on dual-server architecture. Janus ensures security against up to $n-2$ colluding clients (where $n$ is the total client count), which prevents privacy breaches for non-colluders. Additionally, Janus is model-independent, ensuring applicability across any FL model without specific adaptations. Furthermore, Janus introduces a new cryptographic primitive, Separable Homomorphic Commitment, which enables clients to efficiently verify the correctness of aggregation. Finally, extensive experiments show that Janus not only significantly enhances security but also reduces per-client communication and computation overhead from logarithmic to constant scale, with a tolerable impact on model performance.

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AAAI Conference 2025 System Paper

SPASCA: Social Presence and Support with Conversational Agent for Persons Living with Dementia

Ali Köksal
Jingjing Gu
Kotaro Hara
Jing Jiang
Joo-Hwee Lim
Qianli Xu

We present SPASCA - a conversational AI system that promotes psychological and cognitive well-being of persons living with dementia (PLWD). This system features an AI agent that provides social presence and support to PLWD through verbal communications, without physical presence of human caregivers. The system integrates (1) a novel dialogue model that generates dialogue items relevant to the user's experiences and lifestyle, (2) a digital avatar in the form of a talking head with the identity of a caregiver who is familiar to the demented user. We develop prototypes that adopt various interaction modalities and conversational styles and report the pros and cons of different system configurations through expert review. Our system shows the potential of conversational AI for personalized and affordable healthcare services.

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AAAI Conference 2024 Conference Paper

Explainable Origin-Destination Crowd Flow Interpolation via Variational Multi-Modal Recurrent Graph Auto-Encoder

Qiang Zhou
Xinjiang Lu
Jingjing Gu
Zhe Zheng
Bo Jin
Jingbo Zhou

Origin-destination (OD) crowd flow, if more accurately inferred at a fine-grained level, has the potential to enhance the efficacy of various urban applications. While in practice for mining OD crowd flow with effect, the problem of spatially interpolating OD crowd flow occurs since the ineluctable missing values. This problem is further complicated by the inherent scarcity and noise nature of OD crowd flow data. In this paper, we propose an uncertainty-aware interpolative and explainable framework, namely UApex, for realizing reliable and trustworthy OD crowd flow interpolation. Specifically, we first design a Variational Multi-modal Recurrent Graph Auto-Encoder (VMR-GAE) for uncertainty-aware OD crowd flow interpolation. A key idea here is to formulate the problem as semi-supervised learning on directed graphs. Next, to mitigate the data scarcity, we incorporate a distribution alignment mechanism that can introduce supplementary modals into variational inference. Then, a dedicated decoder with a Poisson prior is proposed for OD crowd flow interpolation. Moreover, to make VMR-GAE more trustworthy, we develop an efficient and uncertainty-aware explainer that can provide explanations from the spatiotemporal topology perspective via the Shapley value. Extensive experiments on two real-world datasets validate that VMR-GAE outperforms the state-of-the-art baselines. Also, an exploratory empirical study shows that the proposed explainer can generate meaningful spatiotemporal explanations.

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JBHI Journal 2022 Journal Article

HarMI: Human Activity Recognition Via Multi-Modality Incremental Learning

Xiao Zhang
Hongzheng Yu
Yang Yang
Jingjing Gu
Yujun Li
Fuzhen Zhuang
Dongxiao Yu
Zhaochun Ren

Nowadays, with the development of various kinds of sensors in smartphones or wearable devices, human activity recognition (HAR) has been widely researched and has numerous applications in healthcare, smart city, etc. Many techniques based on hand-crafted feature engineering or deep neural network have been proposed for sensor based HAR. However, these existing methods usually recognize activities offline, which means the whole data should be collected before training, occupying large-capacity storage space. Moreover, once the offline model training finished, the trained model can’t recognize new activities unless retraining from the start, thus with a high cost of time and space. In this paper, we propose a multi-modality incremental learning model, called HarMI, with continuous learning ability. The proposed HarMI model can start training quickly with little storage space and easily learn new activities without storing previous training data. In detail, we first adopt attention mechanism to align heterogeneous sensor data with different frequencies. In addition, to overcome catastrophic forgetting in incremental learning, HarMI utilizes the elastic weight consolidation and canonical correlation analysis from a multi-modality perspective. Extensive experiments based on two public datasets demonstrate that HarMI can achieve a superior performance compared with several state-of-the-arts.

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AAAI Conference 2021 Conference Paper

Modeling Heterogeneous Relations across Multiple Modes for Potential Crowd Flow Prediction

Qiang Zhou
Jingjing Gu
Xinjiang Lu
Fuzhen Zhuang
Yanchao Zhao
Qiuhong Wang
Xiao Zhang

Potential crowd flow prediction for new planned transportationsites is a fundamental task for urban planners and administrators. Intuitively, the potential crowd flow of the new comingsite can be implied by exploring the nearby sites. However, the transportation modes of nearby sites (e. g. bus stations, bicycle stations) might be different from the target site (e. g. subway station), which results in severe data scarcity issues. To this end, we propose a data-driven approach, named MOHER, to predict the potential crowd flow in a certain mode for a new planned site. Specifically, we first identify the neighbor regions of the target site by examining the geographical proximity as well as the urban function similarity. Then, to aggregate these heterogeneous relations, we devise a cross-mode relational GCN, a novel relation-specific transformation model, which can learn not only the correlations but also the differences between different transportation modes. Afterward, we design an aggregator for inductive potential flow representation. Finally, an LTSM module is used for sequential flow prediction. Extensive experiments on realworld data sets demonstrate the superiority of the MOHER framework comparedwith the state-of-the-art algorithms.

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AAAI Conference 2021 Conference Paper

Out-of-Town Recommendation with Travel Intention Modeling

Haoran Xin
Xinjiang Lu
Tong Xu
Hao Liu
Jingjing Gu
Dejing Dou
Hui Xiong

Out-of-town recommendation is designed for those users who leave their home-town areas and visit the areas they have never been to before. It is challenging to recommend Pointof-Interests (POIs) for out-of-town users since the out-oftown check-in behavior is determined by not only the user’s home-town preference but also the user’s travel intention. Besides, the user’s travel intentions are complex and dynamic, which leads to big difficulties in understanding such intentions precisely. In this paper, we propose a TRAvel- INtention-aware Out-of-town Recommendation framework, named TRAINOR. The proposed TRAINOR framework distinguishes itself from existing out-of-town recommenders in three aspects. First, graph neural networks are explored to represent users’ home-town check-in preference and geographical constraints in out-of-town check-in behaviors. Second, a user-specific travel intention is formulated as an aggregation combining home-town preference and generic travel intention together, where the generic travel intention is regarded as a mixture of inherent intentions that can be learned by Neural Topic Model (NTM). Third, a non-linear mapping function, as well as a matrix factorization method, are employed to transfer users’ home-town preference and estimate out-of-town POI’s representation, respectively. Extensive experiments on real-world data sets validate the effectiveness of the TRAINOR framework. Moreover, the learned travel intention can deliver meaningful explanations for understanding a user’s travel purposes.

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IJCAI Conference 2020 Conference Paper

Why We Go Where We Go: Profiling User Decisions on Choosing POIs

Renjun Hu
Xinjiang Lu
Chuanren Liu
Yanyan Li
Hao Liu
Jingjing Gu
Shuai Ma
Hui Xiong

While Point-of-Interest (POI) recommendation has been a popular topic of study for some time, little progress has been made for understanding why and how people make their decisions for the selection of POIs. To this end, in this paper, we propose a user decision profiling framework, named PROUD, which can identify the key factors in people's decisions on choosing POIs. Specifically, we treat each user decision as a set of factors and provide a method for learning factor embeddings. A unique perspective of our approach is to identify key factors, while preserving decision structures seamlessly, via a novel scalar projection maximization objective. Exactly solving the objective is non-trivial due to a sparsity constraint. To address this, our PROUD adopts a self projection attention and an L2 regularized sparse activation to directly estimate the likelihood of each factor to be a key factor. Finally, extensive experiments on real-world data validate the advantage of PROUD in preserving user decision structures. Also, our case study indicates that the identified key decision factors can help us to provide more interpretable recommendations and analyses.

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AAAI Conference 2019 Conference Paper

Joint Representation Learning for Multi-Modal Transportation Recommendation

Hao Liu
Ting Li
Renjun Hu
Yanjie Fu
Jingjing Gu
Hui Xiong

Multi-modal transportation recommendation has a goal of recommending a travel plan which considers various transportation modes, such as walking, cycling, automobile, and public transit, and how to connect among these modes. The successful development of multi-modal transportation recommendation systems can help to satisfy the diversified needs of travelers and improve the efficiency of transport networks. However, existing transport recommender systems mainly focus on unimodal transport planning. To this end, in this paper, we propose a joint representation learning framework for multi-modal transportation recommendation based on a carefully-constructed multi-modal transportation graph. Specifically, we first extract a multi-modal transportation graph from large-scale map query data to describe the concurrency of users, Origin-Destination (OD) pairs, and transport modes. Then, we provide effective solutions for the optimization problem and develop an anchor embedding for transport modes to initialize the embeddings of transport modes. Moreover, we infer user relevance and OD pair relevance, and incorporate them to regularize the representation learning. Finally, we exploit the learned representations for online multimodal transportation recommendations. Indeed, our method has been deployed into one of the largest navigation Apps to serve hundreds of millions of users, and extensive experimental results with real-world map query data demonstrate the enhanced performance of the proposed method for multimodal transportation recommendations.

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AAAI Conference 2019 Conference Paper

Modelling of Bi-Directional Spatio-Temporal Dependence and Users’ Dynamic Preferences for Missing POI Check-In Identification

Dongbo Xi
Fuzhen Zhuang
Yanchi Liu
Jingjing Gu
Hui Xiong
Qing He

Human mobility data accumulated from Point-of-Interest (POI) check-ins provides great opportunity for user behavior understanding. However, data quality issues (e. g. , geolocation information missing, unreal check-ins, data sparsity) in real-life mobility data limit the effectiveness of existing POIoriented studies, e. g. , POI recommendation and location prediction, when applied to real applications. To this end, in this paper, we develop a model, named Bi-STDDP, which can integrate bi-directional spatio-temporal dependence and users’ dynamic preferences, to identify the missing POI check-in where a user has visited at a specific time. Specifically, we first utilize bi-directional global spatial and local temporal information of POIs to capture the complex dependence relationships. Then, target temporal pattern in combination with user and POI information are fed into a multi-layer network to capture users’ dynamic preferences. Moreover, the dynamic preferences are transformed into the same space as the dependence relationships to form the final model. Finally, the proposed model is evaluated on three large-scale real-world datasets and the results demonstrate significant improvements of our model compared with state-of-the-art methods. Also, it is worth noting that the proposed model can be naturally extended to address POI recommendation and location prediction tasks with competitive performances.

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