Author name cluster

Dingqi Yang

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.

14 papers

1 author row

AAAI Conference 2026 Conference Paper

Explicit Intent-Enhanced Knowledge Distillation for Trip Recommendation

Shuliang Wang
Xiaoting Leng
Sijie Ruan
Dingqi Yang
Yicheng Tang
Qianyu Yang
Qianxiong Xu
Jiabao Zhu

Trip recommendation aims to generate a sequence of points of interest (POIs) under a user's query input. Existing data-driven methods mainly fall into two categories: supervised approaches and self-supervised approaches. The former cannot fully capture the transition patterns among POIs, while the latter fail to comprehensively model user's query intents. Fortunately, privileged knowledge distillation (PKD) provides us an unique opportunity to align user's query intents with its corresponding trip in historical data. However, such knowledge alignment is implicit, which may not directly reflect the query intents. To this end, in this paper, we propose EKD-Trip, an explicit intent-enhanced knowledge distillation framework. EKD-Trip first trains a trajectory encoder (teacher model) and a trip generator jointly in a self-supervised manner. Then, a query encoder (student model) is trained via multi-task learning to extract implicit knowledge by PKD from teacher and explicit knowledge from an auxiliary task, respectively. At inference time, we use the query encoder and the trip generator to recommend trips. Extensive experiments on four real-world datasets demonstrate that EKD-Trip outperforms all baselines over three metrics, with a particularly notable improvement of 13.70% in pairs-F1.

PDF Details DOI

TIST Journal 2025 Journal Article

Balancing Cooperation and Competition: Selfish Worker Coalition Formation in Spatial Crowdsourcing

Liang Wang
Shan Su
Rongchang Cheng
Dingqi Yang
Lianbo Ma
Fei Xiong
Bin Guo
Zhiwen Yu

Spatial Crowdsourcing (SC), which outsources location-dependent tasks to workers for physical completion, is gaining popularity. Recently, more complex tasks have emerged that require a group of workers collaborating in a coalition. Several pioneering studies have examined this issue using the server assigned tasks mode from an overall perspective, such as maximizing the total benefits of all workers. Unfortunately, maximizing the overall benefit does not necessarily align with maximizing individual benefits. In practice, crowd workers are often self-interested and autonomous, making decisions based on their personal perspectives. In this article, under the worker selected tasks mode, we investigate an important problem: Selfish Workers Coalition Formation (SWCF) problem in SC. Here, selfish workers autonomously form coalitions to accomplish tasks to maximize their individual benefits. Achieving a stable coalition formation for SWCF problem requires balancing cooperation and competition. First, we transform the SWCF problem into a hedonic coalition formation game using a devised exploited skills-based reward distribution model. Subsequently, we propose a distributed algorithm HCFTA and prove its Nash stability and performance bounds. Additionally, to enhance coalition formation efficiency, we propose a Markov blanket coloring parallel optimization algorithm MCPHCF. Extensive experiments demonstrate the superiority of the proposed methods on both synthetic and real-world datasets.

Details DOI

AAAI Conference 2025 Conference Paper

CasFT: Future Trend Modeling for Information Popularity Prediction with Dynamic Cues-Driven Diffusion Models

Xin Jing
Yichen Jing
Yuhuan Lu
Bangchao Deng
Xueqin Chen
Dingqi Yang

The rapid spread of diverse information on online social platforms has prompted both academia and industry to realize the importance of predicting content popularity, which could benefit a wide range of applications, such as recommendation systems and strategic decision-making. Recent works mainly focused on extracting spatiotemporal patterns inherent in the information diffusion process within a given observation period so as to predict its popularity over a future period of time. However, these works often overlook the future popularity trend, as future popularity could either increase exponentially or stagnate, introducing uncertainties to the prediction performance. Additionally, how to transfer the preceding-term dynamics learned from the observed diffusion process into future-term trends remains an unexplored challenge. Against this background, we propose CasFT, which leverages observed information Cascades and dynamic cues extracted via neural ODEs as conditions to guide the generation of Future popularity-increasing Trends through a diffusion model. These generated trends are then combined with the spatiotemporal patterns in the observed information cascade to make the final popularity prediction. Extensive experiments conducted on three real-world datasets demonstrate that CasFT significantly improves the prediction accuracy compared to state-of-the-art approaches.

PDF Details DOI

IJCAI Conference 2024 Conference Paper

Make Graph Neural Networks Great Again: A Generic Integration Paradigm of Topology-Free Patterns for Traffic Speed Prediction

Yicheng Zhou
Pengfei Wang
Hao Dong
Denghui Zhang
Dingqi Yang
Yanjie Fu
Pengyang Wang

Urban traffic speed prediction aims to estimate the future traffic speed for improving urban transportation services. Enormous efforts have been made to exploit Graph Neural Networks (GNNs) for modeling spatial correlations and temporal dependencies of traffic speed evolving patterns, regularized by graph topology. While achieving promising results, current traffic speed prediction methods still suffer from ignoring topology-free patterns, which cannot be captured by GNNs. To tackle this challenge, we propose a generic model for enabling the current GNN-based methods to preserve topology-free patterns. Specifically, we first develop a Dual Cross-Scale Transformer (DCST) architecture, including a Spatial Transformer and a Temporal Transformer, to preserve the cross-scale topology-free patterns and associated dynamics, respectively. Then, to further integrate both topology-regularized/-free patterns, we propose a distillation-style learning framework, in which the existing GNN-based methods are considered as the teacher model, and the proposed DCST architecture is considered as the student model. The teacher model would inject the learned topology-regularized patterns into the student model for integrating topology-free patterns. The extensive experimental results demonstrated the effectiveness of our methods.

PDF Details DOI

AAAI Conference 2024 Conference Paper

Spatial-Temporal Interplay in Human Mobility: A Hierarchical Reinforcement Learning Approach with Hypergraph Representation

Zhaofan Zhang
Yanan Xiao
Lu Jiang
Dingqi Yang
Minghao Yin
Pengyang Wang

In the realm of human mobility, the decision-making process for selecting the next-visit location is intricately influenced by a trade-off between spatial and temporal constraints, which are reflective of individual needs and preferences. This trade-off, however, varies across individuals, making the modeling of these spatial-temporal dynamics a formidable challenge. To address the problem, in this work, we introduce the "Spatial-temporal Induced Hierarchical Reinforcement Learning" (STI-HRL) framework, for capturing the interplay between spatial and temporal factors in human mobility decision-making. Specifically, STI-HRL employs a two-tiered decision-making process: the low-level focuses on disentangling spatial and temporal preferences using dedicated agents, while the high-level integrates these considerations to finalize the decision. To complement the hierarchical decision setting, we construct a hypergraph to organize historical data, encapsulating the multi-aspect semantics of human mobility. We propose a cross-channel hypergraph embedding module to learn the representations as the states to facilitate the decision-making cycle. Our extensive experiments on two real-world datasets validate the superiority of STI-HRL over state-of-the-art methods in predicting users' next visits across various performance metrics.

PDF Details DOI

IJCAI Conference 2023 Conference Paper

Black-Box Data Poisoning Attacks on Crowdsourcing

Pengpeng Chen
Yongqiang Yang
Dingqi Yang
Hailong Sun
Zhijun Chen
Peng Lin

Understanding the vulnerability of label aggregation against data poisoning attacks is key to ensuring data quality in crowdsourced label collection. State-of-the-art attack mechanisms generally assume full knowledge of the aggregation models while failing to consider the flexibility of malicious workers in selecting which instances to label. Such a setup limits the applicability of the attack mechanisms and impedes further improvement of their success rate. This paper introduces a black-box data poisoning attack framework that finds the optimal strategies for instance selection and labeling to attack unknown label aggregation models in crowdsourcing. We formulate the attack problem on top of a generic formalization of label aggregation models and then introduce a substitution approach that attacks a substitute aggregation model in replacement of the unknown model. Through extensive validation on multiple real-world datasets, we demonstrate the effectiveness of both instance selection and model substitution in improving the success rate of attacks.

PDF Details DOI

TIST Journal 2023 Journal Article

Robust Location Prediction over Sparse Spatiotemporal Trajectory Data: Flashback to the Right Moment!

Bangchao Deng
Dingqi Yang
Bingqing Qu
Benjamin Fankhauser
Philippe Cudre-Mauroux

As a fundamental problem in human mobility modeling, location prediction forecasts a user’s next location based on historical user mobility trajectories. Recurrent neural networks (RNNs) have been widely used to capture sequential patterns of user visited locations for solving location prediction problems. Due to the sparse nature of real-world user mobility trajectories, existing techniques strive to improve RNNs by incorporating spatiotemporal contexts into the recurrent hidden state passing process of RNNs using context-parameterized transition matrices or gates. However, such a scheme mismatches universal spatiotemporal mobility laws and thus cannot fully benefit from rich spatiotemporal contexts encoded in user mobility trajectories. Against this background, we propose Flashback++, a general RNN architecture designed for modeling sparse user mobility trajectories. It not only leverages rich spatiotemporal contexts to search past hidden states with high predictive power but also learns to optimally combine them via a hidden state re-weighting mechanism, which significantly improves the robustness of the models against different settings and datasets. Our extensive evaluation compares Flashback++ against a sizable collection of state-of-the-art techniques on two real-world location-based social networks datasets and one on-campus mobility dataset. Results show that Flashback++ not only consistently and significantly outperforms all baseline techniques by 20.56% to 44.36% but also achieves better robustness of location prediction performance against different model settings (different RNN architectures and numbers of hidden states to flash back), different levels of trajectory sparsity, and different train-testing splitting ratios than baselines, yielding an improvement of 31.05% to 94.60%.

Details DOI

TIST Journal 2022 Journal Article

Data-driven Targeted Advertising Recommendation System for Outdoor Billboard

Liang Wang
Zhiwen Yu
Bin Guo
Dingqi Yang
Lianbo Ma
Zhidan Liu
Fei Xiong

In this article, we propose and study a novel data-driven framework for Targeted Outdoor Advertising Recommendation (TOAR) with a special consideration of user profiles and advertisement topics. Given an advertisement query and a set of outdoor billboards with different spatial locations and rental prices, our goal is to find a subset of billboards, such that the total targeted influence is maximum under a limited budget constraint. To achieve this goal, we are facing two challenges: (1) it is difficult to estimate targeted advertising influence in physical world; (2) due to NP hardness, many common search techniques fail to provide a satisfied solution with an acceptable time, especially for large-scale problem settings. Taking into account the exposure strength, advertisement matching degree, and advertising repetition effect, we first build a targeted influence model that can characterize that the advertising influence spreads along with users mobility. Subsequently, based on a divide-and-conquer strategy, we develop two effective approaches, i.e., a master–slave-based sequential optimization method, TOAR-MSS, and a cooperative co-evolution-based optimization method, TOAR-CC, to solve our studied problem. Extensive experiments on two real-world datasets clearly validate the effectiveness and efficiency of our proposed approaches.

Details DOI

IS Journal 2021 Journal Article

Location-Centric Social Media Analytics: Challenges and Opportunities for Smart Cities

Dingqi Yang
Bingqing Qu
Philippe Cudre-Mauroux

With the proliferation of increasingly powerful smartphones, location-centric social media platforms, such as Foursquare, have attracted millions of users sharing their physical activity online, resulting in an invaluable source of fine-grained, semantically rich, and spatiotemporal user activity data. Such data provides us with an unprecedented opportunity for analyzing urban dynamics and developing smart city applications. In this article, we first systematically discuss the unique characteristics of location-centric social media data, which consist of four data dimensions, i. e. , spatial, temporal, semantic, and social dimensions. We then highlight three key challenges relating to data analytics, i. e. , data heterogeneity, data quality, and privacy. Finally, we discuss the opportunities of leveraging location-centric social media data for urban analytics and smart cities, including both data analytics within and across the four data dimensions, and data fusion with further urban data.

Details DOI

IJCAI Conference 2020 Conference Paper

Location Prediction over Sparse User Mobility Traces Using RNNs: Flashback in Hidden States!

Dingqi Yang
Benjamin Fankhauser
Paolo Rosso
Philippe Cudre-Mauroux

Location prediction is a key problem in human mobility modeling, which predicts a user's next location based on historical user mobility traces. As a sequential prediction problem by nature, it has been recently studied using Recurrent Neural Networks (RNNs). Due to the sparsity of user mobility traces, existing techniques strive to improve RNNs by considering spatiotemporal contexts. The most adopted scheme is to incorporate spatiotemporal factors into the recurrent hidden state passing process of RNNs using context-parameterized transition matrices or gates. However, such a scheme oversimplifies the temporal periodicity and spatial regularity of user mobility, and thus cannot fully benefit from rich historical spatiotemporal contexts encoded in user mobility traces. Against this background, we propose Flashback, a general RNN architecture designed for modeling sparse user mobility traces by doing flashbacks on hidden states in RNNs. Specifically, Flashback explicitly uses spatiotemporal contexts to search past hidden states with high predictive power (i. e. , historical hidden states sharing similar contexts as the current one) for location prediction, which can then directly benefit from rich spatiotemporal contexts. Our extensive evaluation compares Flashback against a sizable collection of state-of-the-art techniques on two real-world LBSN datasets. Results show that Flashback consistently and significantly outperforms state-of-the-art RNNs involving spatiotemporal factors by 15. 9% to 27. 6% in the next location prediction task.

PDF Details DOI

AAAI Conference 2018 Conference Paper

Geographic Differential Privacy for Mobile Crowd Coverage Maximization

Leye Wang
Gehua Qin
Dingqi Yang
Xiao Han
Xiaojuan Ma

For real-world mobile applications such as location-based advertising and spatial crowdsourcing, a key to success is targeting mobile users that can maximally cover certain locations in a future period. To ﬁnd an optimal group of users, existing methods often require information about users’ mobility history, which may cause privacy breaches. In this paper, we propose a method to maximize mobile crowd’s future location coverage under a guaranteed location privacy protection scheme. In our approach, users only need to upload one of their frequently visited locations, and more importantly, the uploaded location is obfuscated using a geographic differential privacy policy. We propose both analytic and practical solutions to this problem. Experiments on real user mobility datasets show that our method signiﬁcantly outperforms the state-of-the-art geographic differential privacy methods by achieving a higher coverage under the same level of privacy protection.

PDF Details

TIST Journal 2017 Journal Article

SPACE-TA

Leye Wang
Daqing Zhang
Dingqi Yang
Animesh Pathak
Chao Chen
Xiao Han
Haoyi Xiong
Yasha Wang

Data quality and budget are two primary concerns in urban-scale mobile crowdsensing. Traditional research on mobile crowdsensing mainly takes sensing coverage ratio as the data quality metric rather than the overall sensed data error in the target-sensing area. In this article, we propose to leverage spatiotemporal correlations among the sensed data in the target-sensing area to significantly reduce the number of sensing task assignments. In particular, we exploit both intradata correlations within the same type of sensed data and interdata correlations among different types of sensed data in the sensing task. We propose a novel crowdsensing task allocation framework called SPACE-TA (SPArse Cost-Effective Task Allocation), combining compressive sensing, statistical analysis, active learning, and transfer learning, to dynamically select a small set of subareas for sensing in each timeslot (cycle), while inferring the data of unsensed subareas under a probabilistic data quality guarantee. Evaluations on real-life temperature, humidity, air quality, and traffic monitoring datasets verify the effectiveness of SPACE-TA. In the temperature-monitoring task leveraging intradata correlations, SPACE-TA requires data from only 15.5% of the subareas while keeping the inference error below 0.25°C in 95% of the cycles, reducing the number of sensed subareas by 18.0% to 26.5% compared to baselines. When multiple tasks run simultaneously, for example, for temperature and humidity monitoring, SPACE-TA can further reduce ∼10% of the sensed subareas by exploiting interdata correlations.

Details DOI

TIST Journal 2016 Journal Article

Participatory Cultural Mapping Based on Collective Behavior Data in Location-Based Social Networks

Dingqi Yang
Daqing Zhang
Bingqing Qu

Culture has been recognized as a driving impetus for human development. It co-evolves with both human belief and behavior. When studying culture, Cultural Mapping is a crucial tool to visualize different aspects of culture (e.g., religions and languages) from the perspectives of indigenous and local people. Existing cultural mapping approaches usually rely on large-scale survey data with respect to human beliefs, such as moral values. However, such a data collection method not only incurs a significant cost of both human resources and time, but also fails to capture human behavior, which massively reflects cultural information. In addition, it is practically difficult to collect large-scale human behavior data. Fortunately, with the recent boom in Location-Based Social Networks (LBSNs), a considerable number of users report their activities in LBSNs in a participatory manner, which provides us with an unprecedented opportunity to study large-scale user behavioral data. In this article, we propose a participatory cultural mapping approach based on collective behavior in LBSNs. First, we collect the participatory sensed user behavioral data from LBSNs. Second, since only local users are eligible for cultural mapping, we propose a progressive “home” location identification method to filter out ineligible users. Third, by extracting three key cultural features from daily activity, mobility, and linguistic perspectives, respectively, we propose a cultural clustering method to discover cultural clusters. Finally, we visualize the cultural clusters on the world map. Based on a real-world LBSN dataset, we experimentally validate our approach by conducting both qualitative and quantitative analysis on the generated cultural maps. The results show that our approach can subtly capture cultural features and generate representative cultural maps that correspond well with traditional cultural maps based on survey data.

Details DOI

IJCAI Conference 2016 Conference Paper

POISketch: Semantic Place Labeling over User Activity Streams

Dingqi Yang
Bin Li
Philippe Cudr
eacute; -Mauroux

Capturing place semantics is critical for enabling location-based applications. Techniques for assigning semantic labels (e. g. , "bar" or "office") to unlabeled places mainly resort to mining user activity logs by exploiting visiting patterns. However, existing approaches focus on inferring place labels with a static user activity dataset, and ignore the visiting pattern dynamics in user activity streams, leading to the rapid decrease of labeling accuracy over time. In this paper, we tackle the problem of semantic place labeling over user activity streams. We formulate this problem as a classification problem by characterizing each place through its fine-grained visiting patterns, which encode the visiting frequency of each user in each typical time slot. However, with the incoming activities of new users in data streams, such fine-grained visiting patterns constantly grow, leading to a continuously expanding feature space. To solve this issue, we propose an updatable sketching technique that creates and incrementally updates a set of compact and fixed-size sketches to approximate the similarity between fine-grained visiting patterns of ever-growing size. We further consider the discriminative weights of user activities in place labeling, and seamlessly incorporate them into our sketching method. Our empirical evaluation on real-world datasets demonstrates the validity of our approach and shows that sketches can be efficiently and effectively used to infer place labels over user activity streams.

PDF Details