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Zipei Fan

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9 papers

2 author rows

AAAI Conference 2026 Conference Paper

Multifaceted Scenario-Aware Hypergraph Learning for Next POI Recommendation

Yuxi Lin
Yongkang Li
Jie Xing
Zipei Fan

Among the diverse services provided by Location-Based Social Networks (LBSNs), Next Point-of-Interest (POI) recommendation plays a crucial role in inferring user preferences from historical check-in trajectories. However, existing sequential and graph-based methods frequently neglect significant mobility variations across distinct contextual scenarios (e.g., tourists versus locals). This oversight results in suboptimal performance due to two fundamental limitations: the inability to capture scenario-specific features and the failure to resolve inherent inter-scenario conflicts. To overcome these limitations, we propose the Multifaceted Scenario-Aware Hypergraph Learning method (MSAHG), a framework that adopts a scenario-splitting paradigm for next POI recommendation. Our main contributions are: (1) Construction of scenario-specific, multi-view disentangled sub-hypergraphs to capture distinct mobility patterns; (2) A parameter-splitting mechanism to adaptively resolve conflicting optimization directions across scenarios while preserving generalization capability. Extensive experiments on three real-world datasets demonstrate that MSAHG consistently outperforms five state-of-the-art methods across diverse scenarios, confirming its effectiveness in multi-scenario POI recommendation.

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NeurIPS Conference 2025 Conference Paper

KaRF: Weakly-Supervised Kolmogorov-Arnold Networks-based Radiance Fields for Local Color Editing

Wudi Chen
Zhiyuan Zha
Shigang Wang
Bihan Wen
Xin Yuan
Jiantao Zhou
Zipei Fan
Gang Yan

Recent advancements have suggested that neural radiance fields (NeRFs) show great potential in color editing within the 3D domain. However, most existing NeRF-based editing methods continue to face significant challenges in local region editing, which usually lead to imprecise local object boundaries, difficulties in maintaining multi-view consistency, and over-reliance on annotated data. To address these limitations, in this paper, we propose a novel weakly-supervised method called KaRF for local color editing, which facilitates high-fidelity and realistic appearance edits in arbitrary regions of 3D scenes. At the core of the proposed KaRF approach is a unified two-stage Kolmogorov-Arnold Networks (KANs)-based radiance fields framework, comprising a segmentation stage followed by a local recoloring stage. This architecture seamlessly integrates geometric priors from NeRF to achieve weakly-supervised learning, leading to superior performance. More specifically, we propose a residual adaptive gating KAN structure, which integrates KAN with residual connections, adaptive parameters, and gating mechanisms to effectively enhance segmentation accuracy and refine specific editing effects. Additionally, we propose a palette-adaptive reconstruction loss, which can enhance the accuracy of additive mixing results. Extensive experiments demonstrate that the proposed KaRF algorithm significantly outperforms many state-of-the-art methods both qualitatively and quantitatively. Our code and more results are available at: https: //github. com/PaiDii/KARF. git.

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ICRA Conference 2024 Conference Paper

HHGNN: Heterogeneous Hypergraph Neural Network for Traffic Agents Trajectory Prediction in Grouping Scenarios

Hetian Guo
Yingzhi Peng
Zipei Fan
He Zhu
Xuan Song 0001

In many intelligent transportation systems, predicting the future motion of heterogeneous traffic participants is a fundamental but challenging task due to various factors encompassing the agents’ dynamic states, interactions with neighboring agents and surrounding traffic infrastructures, and their stochastic and multi-modal natural behavior tendencies. However, existing approaches have limitations as they either focus solely on static, pairwise interactions, ignoring interactions of varied granularity, or fail to tackle agents’ heterogeneity. In this paper, instead of focusing solely on pairwise interactions, we propose a Heterogenous Hypergraph Graph Neural Network (HHGNN) based motion prediction model that leverages the nature of hypergraph to encode the groupwise interactions among traffic participants. Moreover, we propose the type-aware two-level hypergraph message passing module (TTHMS) with learnable hyperedge-type embeddings to model the intra-group and inter-group level interactions among heterogeneous traffic agents (e. g. , vehicles, pedestrians, and cyclists). Besides, We integrate a scene context fusion layer in TTHMS to incorporate the scene context. Comparison and ablation experiments on the Waymo Open Motion Dataset (WOMD) demonstrate HHGNN’s effectiveness within the motion prediction task.

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NeurIPS Conference 2024 Conference Paper

Taming the Long Tail in Human Mobility Prediction

Xiaohang Xu
Renhe Jiang
Chuang Yang
Zipei Fan
Kaoru Sezaki

With the popularity of location-based services, human mobility prediction plays a key role in enhancing personalized navigation, optimizing recommendation systems, and facilitating urban mobility and planning. This involves predicting a user's next POI (point-of-interest) visit using their past visit history. However, the uneven distribution of visitations over time and space, namely the long-tail problem in spatial distribution, makes it difficult for AI models to predict those POIs that are less visited by humans. In light of this issue, we propose the $\underline{\bf{Lo}}$ng-$\underline{\bf{T}}$ail Adjusted $\underline{\bf{Next}}$ POI Prediction (LoTNext) framework for mobility prediction, combining a Long-Tailed Graph Adjustment module to reduce the impact of the long-tailed nodes in the user-POI interaction graph and a novel Long-Tailed Loss Adjustment module to adjust loss by logit score and sample weight adjustment strategy. Also, we employ the auxiliary prediction task to enhance generalization and accuracy. Our experiments with two real-world trajectory datasets demonstrate that LoTNext significantly surpasses existing state-of-the-art works.

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AAAI Conference 2023 Conference Paper

Easy Begun Is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout

Hongjun Wang
Jiyuan Chen
Tong Pan
Zipei Fan
Xuan Song
Renhe Jiang
Lingyu Zhang
Yi Xie

Spatial-temporal (ST) graph modeling, such as traffic speed forecasting and taxi demand prediction, is an important task in deep learning area. However, for the nodes in the graph, their ST patterns can vary greatly in difficulties for modeling, owning to the heterogeneous nature of ST data. We argue that unveiling the nodes to the model in a meaningful order, from easy to complex, can provide performance improvements over traditional training procedure. The idea has its root in Curriculum Learning, which suggests in the early stage of training models can be sensitive to noise and difficult samples. In this paper, we propose ST-Curriculum Dropout, a novel and easy-to-implement strategy for spatial-temporal graph modeling. Specifically, we evaluate the learning difficulty of each node in high-level feature space and drop those difficult ones out to ensure the model only needs to handle fundamental ST relations at the beginning, before gradually moving to hard ones. Our strategy can be applied to any canonical deep learning architecture without extra trainable parameters, and extensive experiments on a wide range of datasets are conducted to illustrate that, by controlling the difficulty level of ST relations as the training progresses, the model is able to capture better representation of the data and thus yields better generalization.

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TIST Journal 2022 Journal Article

Predicting Citywide Crowd Dynamics at Big Events: A Deep Learning System

Renhe Jiang
Zekun CAI
Zhaonan Wang
Chuang Yang
Zipei Fan
Quanjun Chen
Xuan Song
Ryosuke Shibasaki

Event crowd management has been a significant research topic with high social impact. When some big events happen such as an earthquake, typhoon, and national festival, crowd management becomes the first priority for governments (e.g., police) and public service operators (e.g., subway/bus operator) to protect people’s safety or maintain the operation of public infrastructures. However, under such event situations, human behavior will become very different from daily routines, which makes prediction of crowd dynamics at big events become highly challenging, especially at a citywide level. Therefore in this study, we aim to extract the “deep” trend only from the current momentary observations and generate an accurate prediction for the trend in the short future, which is considered to be an effective way to deal with the event situations. Motivated by these, we build an online system called DeepUrbanEvent, which can iteratively take citywide crowd dynamics from the current one hour as input and report the prediction results for the next one hour as output. A novel deep learning architecture built with recurrent neural networks is designed to effectively model these highly complex sequential data in an analogous manner to video prediction tasks. Experimental results demonstrate the superior performance of our proposed methodology to the existing approaches. Lastly, we apply our prototype system to multiple big real-world events and show that it is highly deployable as an online crowd management system.

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AAAI Conference 2020 Conference Paper

Multimodal Interaction-Aware Trajectory Prediction in Crowded Space

Xiaodan Shi
Xiaowei Shao
Zipei Fan
Renhe Jiang
Haoran Zhang
Zhiling Guo
Guangming Wu
Wei Yuan

Accurate human path forecasting in complex and crowded scenarios is critical for collision avoidance of autonomous driving and social robots navigation. It still remains as a challenging problem because of dynamic human interaction and intrinsic multimodality of human motion. Given the observation, there is a rich set of plausible ways for an agent to walk through the circumstance. To address those issues, we propose a spatio-temporal model that can aggregate the information from socially interacting agents and capture the multimodality of the motion patterns. We use mixture density functions to describe the human path and predict the distribution of future paths with explicit density. To integrate more factors to model interacting people, we further introduce a coordinate transformation to represent the relative motion between people. Extensive experiments over several trajectory prediction benchmarks demonstrate that our method is able to forecast various plausible futures in complex scenarios and achieves state-of-the-art performance.

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AAAI Conference 2018 Conference Paper

DeepUrbanMomentum: An Online Deep-Learning System for Short-Term Urban Mobility Prediction

Renhe Jiang
Xuan Song
Zipei Fan
Tianqi Xia
Quanjun Chen
Satoshi Miyazawa
Ryosuke Shibasaki

Big human mobility data are being continuously generated through a variety of sources, some of which can be treated and used as streaming data for understanding and predicting urban dynamics. With such streaming mobility data, the online prediction of short-term human mobility at the city level can be of great signiﬁcance for transportation scheduling, urban regulation, and emergency management. In particular, when big rare events or disasters happen, such as large earthquakes or severe trafﬁc accidents, people change their behaviors from their routine activities. This means people’s movements will almost be uncorrelated with their past movements. Therefore, in this study, we build an online system called DeepUrban- Momentum to conduct the next short-term mobility predictions by using (the limited steps of) currently observed human mobility data. A deep-learning architecture built with recurrent neural networks is designed to effectively model these highly complex sequential data for a huge urban area. Experimental results demonstrate the superior performance of our proposed model as compared to the existing approaches. Lastly, we apply our system to a real emergency scenario and demonstrate that our system is applicable in the real world.

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IJCAI Conference 2016 Conference Paper

A Collaborative Filtering Approach to Citywide Human Mobility Completion from Sparse Call Records

Zipei Fan
Ayumi Arai
Xuan Song
Apichon Witayangkurn
Hiroshi Kanasugi
Ryosuke Shibasaki

Most of human mobility big datasets available by now, for example call detail records or twitter data with geotag, are always sparse and heavily biased. As a result, using such kind of data to directly represent real-world human mobility is unreliable and problematic. However, difficult though it is, a completion of human mobility turns out to be a promising way to minimize the issues of sparsity and bias. In this paper, we model the completion problem as a recommender system and therefore solve this problem in a collaborative filtering (CF) framework. We propose a spatio-temporal CF that simultaneously infers the topic distribution over users, time-of-days, days as well as locations, and then use the topic distributions to estimate a posterior over locations and infer the optimal location sequence in a Hidden Markov Model considering the spatio-temporal continuity. We apply and evaluate our algorithm using a real-world Call Detail Records dataset from Bangladesh and gives an application on Dynamic Census, which incorporates the survey data from cell phone users to generate an hourly population distribution with attributes.

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