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Depeng Jin

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24 papers

2 author rows

AAAI Conference 2026 Conference Paper

ResMAS: Resilience Optimization in LLM-based Multi-agent Systems

Zhilun Zhou
Zihan Liu
Jiahe Liu
Qingyu Shao
Yihan Wang
Kun Shao
Depeng Jin
Fengli Xu

Large Language Model-based Multi-Agent Systems (LLM-based MAS), where multiple LLM agents collaborate to solve complex tasks, have shown impressive performance in many areas. However, MAS are typically distributed across different devices or environments, making them vulnerable to perturbations such as agent failures. While existing works have studied the adversarial attacks and corresponding defense strategies, they mainly focus on reactively detecting and mitigating attacks after they occur rather than proactively designing inherently resilient systems. In this work, we study the resilience of LLM-based MAS under perturbations and find that both the communication topology and prompt design significantly influence system resilience. Motivated by these findings, we propose ResMAS: a two-stage framework for enhancing MAS resilience. First, we train a reward model to predict the MAS’s resilience, based on which we train a topology generator to automatically design resilient topology for specific tasks through reinforcement learning. Second, we introduce a topology-aware prompt optimization method that refines each agent’s prompt based on its connections and interactions with other agents. Extensive experiments across a range of tasks show that our approach substantially improves MAS resilience under various constraints. Moreover, our framework demonstrates strong generalization ability to new tasks and models, highlighting its potential for building resilient MASs.

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NeurIPS Conference 2025 Conference Paper

Diffusion Transformers as Open-World Spatiotemporal Foundation Models

Yuan Yuan
Chonghua Han
Jingtao Ding
Guozhen Zhang
Depeng Jin
Yong Li

The urban environment is characterized by complex spatio-temporal dynamics arising from diverse human activities and interactions. Effectively modeling these dynamics is essential for understanding and optimizing urban systems. In this work, we introduce UrbanDiT, a foundation model for open-world urban spatio-temporal learning that successfully scales up diffusion transformers in this field. UrbanDiT pioneers a unified model that integrates diverse data sources and types while learning universal spatio-temporal patterns across different cities and scenarios. This allows the model to unify both multi-data and multi-task learning, and effectively support a wide range of spatio-temporal applications. Its key innovation lies in the elaborated prompt learning framework, which adaptively generates both data-driven and task-specific prompts, guiding the model to deliver superior performance across various urban applications. UrbanDiT offers three advantages: 1) It unifies diverse data types, such as grid-based and graph-based data, into a sequential format; 2) With task-specific prompts, it supports a wide range of tasks, including bi-directional spatio-temporal prediction, temporal interpolation, spatial extrapolation, and spatio-temporal imputation; and 3) It generalizes effectively to open-world scenarios, with its powerful zero-shot capabilities outperforming nearly all baselines with training data. UrbanDiT sets up a new benchmark for foundation models in the urban spatio-temporal domain. Code and datasets are publicly available at \url{https: //github. com/tsinghua-fib-lab/UrbanDiT}.

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AAAI Conference 2025 Conference Paper

Iterative Sparse Attention for Long-sequence Recommendation

Guanyu Lin
Jinwei Luo
Yinfeng Li
Chen Gao
Qun Luo
Depeng Jin

Longer historical behaviors often improve recommendation accuracy but bring efficient problems. As sequences get longer, the following two main challenges have not been addressed: (1) efficient modeling under increasing sequence length and (2) interest drifting within historical items. In this paper, we propose Iterative Sparse Attention for Long-sequence Recommendation (ISA) with Sparse Attention Layer and Iterative Attention Layer to efficiently capture sequential pattern and expand the receptive field of each historical items. We take the pioneering step to address the efficient and interest drifting challenges for the long-sequence recommendation simultaneously. The theoretical analysis illustrates that our proposed iterative method can approximate full attention efficiently. Experiments on two real-world datasets show the superiority of our proposed method against state-of-the-art baselines.

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ICML Conference 2025 Conference Paper

Reinforcement Learning with Adaptive Reward Modeling for Expensive-to-Evaluate Systems

Hongyuan Su
Yu Zheng 0010
Yuan Yuan 0032
Yuming Lin 0003
Depeng Jin
Yong Li 0008

Training reinforcement learning (RL) agents requires extensive trials and errors, which becomes prohibitively time-consuming in systems with costly reward evaluations. To address this challenge, we propose adaptive reward modeling (AdaReMo) which accelerates RL training by decomposing the complicated reward function into multiple localized fast reward models approximating direct reward evaluation with neural networks. These models dynamically adapt to the agent’s evolving policy by fitting the currently explored subspace with the latest trajectories, ensuring accurate reward estimation throughout the entire training process while significantly reducing computational overhead. We empirically show that AdaReMo not only achieves over 1, 000 times speedup but also improves the performance by 14. 6% over state-of-the-art approaches across three expensive-to-evaluate systems—molecular generation, epidemic control, and spatial planning. Code and data for the project are provided at https: //github. com/tsinghua-fib-lab/AdaReMo.

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ICLR Conference 2025 Conference Paper

Symbolic regression via MDLformer-guided search: from minimizing prediction error to minimizing description length

Zihan Yu
Jingtao Ding
Yong Li 0008
Depeng Jin

Symbolic regression, a task discovering the formula best fitting the given data, is typically based on the heuristical search. These methods usually update candidate formulas to obtain new ones with lower prediction errors iteratively. However, since formulas with similar function shapes may have completely different symbolic forms, the prediction error does not decrease monotonously as the search approaches the target formula, causing the low recovery rate of existing methods. To solve this problem, we propose a novel search objective based on the minimum description length, which reflects the distance from the target and decreases monotonically as the search approaches the correct form of the target formula. To estimate the minimum description length of any input data, we design a neural network, MDLformer, which enables robust and scalable estimation through large-scale training. With the MDLformer's output as the search objective, we implement a symbolic regression method, SR4MDL, that can effectively recover the correct mathematical form of the formula. Extensive experiments illustrate its excellent performance in recovering formulas from data. Our method successfully recovers around 50 formulas across two benchmark datasets comprising 133 problems, outperforming state-of-the-art methods by 43.92%. Experiments on 122 unseen black-box problems further demonstrate its generalization performance. We release our code at https://github.com/tsinghua-fib-lab/SR4MDL .

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TIST Journal 2024 Journal Article

Empowering Predictive Modeling by GAN-based Causal Information Learning

Jinwei Zeng
Guozhen Zhang
Jian Yuan
Yong Li
Depeng Jin

Generally speaking, we can easily specify many causal relationships in the prediction tasks of ubiquitous computing, such as human activity prediction, mobility prediction, and health prediction. However, most of the existing methods in these fields failed to take advantage of this prior causal knowledge. They typically make predictions only based on correlations in the data, which hinders the prediction performance in real-world scenarios, because a distribution shift between training data and testing data generally exists. To fill in this gap, we proposed a Generative Adversarial Network (GAN)-based Causal Information Learning prediction framework, which can effectively leverage causal information to improve the prediction performance of existing ubiquitous computing deep learning models. Specifically, faced with a unique challenge that the treatment variable, referring to the intervention that influences the target in a causal relationship, is generally continuous in ubiquitous computing, the framework employs a representation learning approach with a GAN-based deep learning model. By projecting all variables except the treatment into a latent space, it effectively minimizes confounding bias and leverages the learned latent representation for accurate predictions. In this way, it deals with the continuous treatment challenge, and in the meantime, it can be easily integrated with existing deep learning models to lift their prediction performance in practical scenarios with causal information. Extensive experiments on two large-scale real-world datasets demonstrate its superior performance over multiple state-of-the-art baselines. We also propose an analytical framework together with extensive experiments to empirically show that our framework achieves better performance gain under two conditions: when the distribution differences between the training data and the testing data are more significant and when the treatment effects are larger. Overall, this work suggests that learning causal information is a promising way to improve the prediction performance of ubiquitous computing tasks. We open both our dataset and code 1 and call for more research attention in this area.

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TIST Journal 2024 Journal Article

Fine-grained Courier Delivery Behavior Recovery with a Digital Twin Based Iterative Calibration Framework

Fudan Yu
Guozhen Zhang
Haotian Wang
Depeng Jin
Yong Li

Recovering the fine-grained working process of couriers is becoming one of the essential problems for improving the express delivery systems because knowing the detailed process of how couriers accomplish their daily work facilitates the analyzing, understanding, and optimizing of the working procedure. Although coarse-grained courier trajectories and waybill delivery time data can be collected, this problem is still challenging due to noisy data with spatio-temporal biases, lacking ground truth of couriers’ fine-grained behaviors, and complex correlations between behaviors. Existing works typically focus on a single dimension of the process such as inferring the delivery time and can only yield results of low spatio-temporal resolution, which cannot address the problem well. To bridge the gap, we propose a digital-twin-based iterative calibration system (DTRec) for fine-grained courier working process recovery. We first propose a spatio-temporal bias correction algorithm, which systematically improves existing methods in correcting waybill addresses and trajectory stay points. Second, to model the complex correlations among behaviors and inherent physical constraints, we propose an agent-based model to build the digital twin of couriers. Third, to further improve recovery performance, we design a digital-twin-based iterative calibration framework, which leverages the inconsistency between the deduction results of the digital twin and the recovery results from real-world data to improve both the agent-based model and the recovery results. Experiments show that DTRec outperforms state-of-the-art baselines by 10.8% in terms of fine-grained accuracy on real-world datasets. The system is deployed in the industrial practices in JD Logistics with promising applications. The code is available at https://github.com/tsinghua-fib-lab/Courier-DTRec.

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TIST Journal 2024 Journal Article

Generating Daily Activities with Need Dynamics

Yuan Yuan
Jingtao Ding
Huandong Wang
Depeng Jin

Daily activity data recording individuals’ various activities in daily life are widely used in many applications such as activity scheduling, activity recommendation, and policymaking. Though with high value, its accessibility is limited due to high collection costs and potential privacy issues. Therefore, simulating human activities to produce massive high-quality data is of great importance. However, existing solutions, including rule-based methods with simplified behavior assumptions and data-driven methods directly fitting real-world data, both cannot fully qualify for matching reality. In this article, motivated by the classic psychological theory, Maslow’s need theory describing human motivation, we propose a knowledge-driven simulation framework based on generative adversarial imitation learning. Our core idea is to model the evolution of human needs as the underlying mechanism that drives activity generation in the simulation model. Specifically, a hierarchical model structure that disentangles different need levels and the use of neural stochastic differential equations successfully capture the piecewise-continuous characteristics of need dynamics. Extensive experiments demonstrate that our framework outperforms the state-of-the-art baselines regarding data fidelity and utility. We also present the insightful interpretability of the need modeling. Moreover, privacy preservation evaluations validate that the generated data does not leak individual privacy. The code is available at https://github.com/tsinghua-fib-lab/Activity-Simulation-SAND.

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TIST Journal 2024 Journal Article

KGDA: A Knowledge Graph Driven Decomposition Approach for Cellular Traffic Prediction

Jiahui Gong
Tong Li
Huandong Wang
Yu Liu
Xing Wang
Zhendong Wang
Chao Deng
Junlan Feng

Understanding and accurately predicting cellular traffic data is vital for communication operators and device users, as it facilitates efficient resource allocation and ensures superior service quality. However, large-scale cellular traffic data forecasting remains challenging due to intricate temporal variations and complex spatial relationships. This article proposes a Knowledge Graph Driven Decomposition Approach (KGDA) for precise cellular traffic prediction. The KGDA breaks down the impact of static environmental factors and dynamic autocorrelations of cellular traffic time series, enabling the capture of overall traffic changes and understanding of traffic dependence on past values. Specifically, we propose an urban knowledge graph to capture the static environmental context of base stations, mapping these entities into the same latent space while retaining static environmental knowledge. The cellular traffic is divided into a regular pattern and fluctuating residual components, with the KGDA comprising four modules: a Knowledge Graph Representation Learning model, a traffic regular pattern prediction module, a traffic residual dynamic prediction module, and an attentional fusion module. The first leverages graph neural networks to extract spatial contexts and predict regular patterns, the second utilizes the Bi-directional Long Short-Term Memory (Bi-LSTM) model to capture autocorrelations of traffic time series, and the final module integrates the patterns and residuals to produce the final prediction result. Comprehensive experiments demonstrate that our proposed model outperforms state-of-the-art models by more than 10% in forecasting cellular traffic.

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ICLR Conference 2024 Conference Paper

Spatio-Temporal Few-Shot Learning via Diffusive Neural Network Generation

Yuan Yuan 0032
Chenyang Shao
Jingtao Ding
Depeng Jin
Yong Li 0008

Spatio-temporal modeling is foundational for smart city applications, yet it is often hindered by data scarcity in many cities and regions. To bridge this gap, we propose a novel generative pre-training framework, GPD, for spatio-temporal few-shot learning with urban knowledge transfer. Unlike conventional approaches that heavily rely on common feature extraction or intricate few-shot learning designs, our solution takes a novel approach by performing generative pre-training on a collection of neural network parameters optimized with data from source cities. We recast spatio-temporal few-shot learning as pre-training a generative diffusion model, which generates tailored neural networks guided by prompts, allowing for adaptability to diverse data distributions and city-specific characteristics. GPD employs a Transformer-based denoising diffusion model, which is model-agnostic to integrate with powerful spatio-temporal neural networks. By addressing challenges arising from data gaps and the complexity of generalizing knowledge across cities, our framework consistently outperforms state-of-the-art baselines on multiple real-world datasets for tasks such as traffic speed prediction and crowd flow prediction. The implementation of our approach is available: https://github.com/tsinghua-fib-lab/GPD.

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TIST Journal 2023 Journal Article

DAS: Efficient Street View Image Sampling for Urban Prediction

Guozhen Zhang
Jinhui Yi
Jian Yuan
Yong Li
Depeng Jin

Street view data is one of the most common data sources for urban prediction tasks, such as estimating socioeconomic status, sensing physical urban changes, and identifying urban villages. Typical research in this field consists of two steps: acquiring a dataset with a street view image sampling algorithm and designing a prediction algorithm for urban prediction tasks. However, most of the previous research focuses on the prediction algorithms, leaving the sampling algorithms underexplored. To fill this gap, we set out to investigate how different street view image sampling algorithms affect the performance of the follow-up tasks and develop an effective street view image sampling algorithm for urban prediction. Through a comprehensive analysis of the performance of different sampling algorithms in three of the most common urban prediction tasks, including commercial activeness prediction, urban liveliness prediction, and urban population prediction, we provide solid empirical evidence that the sampling algorithm significantly affects the performance of the prediction model. Specifically, the performance differences of different sampling algorithms can reach over 25%. Further, we revealed that the sampling step size and the sampling quality are two important factors that affect the performance of a sampling algorithm, while the sampling angle has little influence. Inspired by our analysis results, we propose an effective street view image sampling algorithm, DAS, which contains a denoising module and an adaptive sampling module. It can dynamically adjust the sampling step size to adapt to the optimal size for each region and get rid of the impact of noise images in the meantime. Experiments on three large-scale datasets demonstrate its superior performance over multiple state-of-the-art baselines, and further ablation study shows the effectiveness of each module. Finally, through a thorough discussion of our findings and experimental results, we provide insights into the street view image sampling algorithm design, and we call for more researches in this blank area.

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TIST Journal 2023 Journal Article

Discovering Causes of Traffic Congestion via Deep Transfer Clustering

Mudan Wang
Yuan Yuan
Huan Yan
HONGJIE SUI
Fan Zuo
Yue Liu
Yong Li
Depeng Jin

Traffic congestion incurs long delay in travel time, which seriously affects our daily travel experiences. Exploring why traffic congestion occurs is significantly important to effectively address the problem of traffic congestion and improve user experience. Traditional approaches to mine the congestion causes depend on human efforts, which is time consuming and cost-intensive. Hence, we aim at discovering the known and unknown causes of traffic congestion in a systematic way. However, to achieve it, there are three challenges: (1) traffic congestion is affected by several factors with complex spatio-temporal relations; (2) there are a few samples of congestion data with known causes due to the limitation of human label; (3) more unknown congestion causes are unexplored since several factors contribute to traffic congestion. To address above challenges, we design a congestion cause discovery system consisting of two modules: (1) congestion feature extraction module, which extracts the important features distinguishing between different causes of congestion; and (2) congestion cause discovery module, which designs a deep semi-supervised learning based framework to discover the causes of traffic congestion with limited labeled data. Specifically, in pre-training stage, it first leverages a few labeled data as prior knowledge to pre-train the model. Then, in clustering stage, we propose two different clustering methods to discover the congestion causes. For the first clustering method, we extend the classic deep embedded clustering model to produce clusters via soft assignment. For the second one, we iteratively use k -means to group the latent features extracted from the pre-trained model, and use the cluster results as pseudo-labels to fine-tune the network. Extensive experiments show that the performance of our methods is superior to the state-of-the-art baselines, which demonstrates the effectiveness of the proposed cause discovery system. Additionally, our system is deployed and used in the practical production environment at Amap.

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AAAI Conference 2023 Conference Paper

PateGail: A Privacy-Preserving Mobility Trajectory Generator with Imitation Learning

Huandong Wang
Changzheng Gao
Yuchen Wu
Depeng Jin
Lina Yao
Yong Li

Generating human mobility trajectories is of great importance to solve the lack of large-scale trajectory data in numerous applications, which is caused by privacy concerns. However, existing mobility trajectory generation methods still require real-world human trajectories centrally collected as the training data, where there exists an inescapable risk of privacy leakage. To overcome this limitation, in this paper, we propose PateGail, a privacy-preserving imitation learning model to generate mobility trajectories, which utilizes the powerful generative adversary imitation learning model to simulate the decision-making process of humans. Further, in order to protect user privacy, we train this model collectively based on decentralized mobility data stored in user devices, where personal discriminators are trained locally to distinguish and reward the real and generated human trajectories. In the training process, only the generated trajectories and their rewards obtained based on personal discriminators are shared between the server and devices, whose privacy is further preserved by our proposed perturbation mechanisms with theoretical proof to satisfy differential privacy. Further, to better model the human decision-making process, we propose a novel aggregation mechanism of the rewards obtained from personal discriminators. We theoretically prove that under the reward obtained based on the aggregation mechanism, our proposed model maximizes the lower bound of the discounted total rewards of users. Extensive experiments show that the trajectories generated by our model are able to resemble real-world trajectories in terms of five key statistical metrics, outperforming state-of-the-art algorithms by over 48.03%. Furthermore, we demonstrate that the synthetic trajectories are able to efficiently support practical applications, including mobility prediction and location recommendation.

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TIST Journal 2022 Journal Article

Crowd Flow Prediction for Irregular Regions with Semantic Graph Attention Network

Fuxian Li
Jie Feng
Huan Yan
Depeng Jin
Yong Li

It is essential to predict crowd flow precisely in a city, which is practically partitioned into irregular regions based on road networks and functionality. However, prior works mainly focus on grid-based crowd flow prediction, where a city is divided into many regular grids. Although Convolutional Neural Netwok (CNN) is powerful to capture spatial dependence from grid-based Euclidean data, it fails to tackle non-Euclidean data, which reflect the correlations among irregular regions. Besides, prior works fail to jointly capture the hierarchical spatio-temporal dependence from both regular and irregular regions. Finally, the correlations among regions are time-varying and functionality-related. However, the combination of dynamic and semantic attributes of regions are ignored by related works. To address the above challenges, in this article, we propose a novel model to tackle the flow prediction task for irregular regions. First, we employ CNN and Graph Neural Network (GNN) to capture micro and macro spatial dependence among grid-based regions and irregular regions, respectively. Further, we think highly of the dynamic inter-region correlations and propose a location-aware and time-aware graph attention mechanism named Semantic Graph Attention Network (Semantic-GAT), based on dynamic node attribute embedding and multi-view graph reconstruction. Extensive experimental results based on two real-life datasets demonstrate that our model outperforms 10 baselines by reducing the prediction error around 8%.

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AAAI Conference 2022 Conference Paper

MAPDP: Cooperative Multi-Agent Reinforcement Learning to Solve Pickup and Delivery Problems

Zefang Zong
Meng Zheng
Yong Li
Depeng Jin

Cooperative Pickup and Delivery Problem (PDP), as a variant of the typical Vehicle Routing Problems (VRP), is an important formulation in many real-world applications, such as on-demand delivery, industrial warehousing, etc. It is of great importance to efficiently provide high-quality solutions of cooperative PDP. However, it is not trivial to provide effective solutions directly due to two major challenges: 1) the structural dependency between pickup and delivery pairs require explicit modeling and representation. 2) the cooperation between different vehicles is highly related to solution exploration and is difficult to model. In this paper, we propose a novel multi-agent reinforcement learning-based framework to solve the cooperative PDP (MAPDP). First, we design a paired context embedding to well measure the dependency of different nodes considering their structural limits. Second, we utilize cooperative multi-agent decoders to leverage the decision dependence among different vehicle agents based on a special communication embedding. Third, we design a novel cooperative A2C algorithm to train the integrated model. We conduct extensive experiments on a randomly generated dataset and a real-world dataset. Experiments result shown that the proposed MAPDP outperforms all other baselines by at least 1. 64% in all settings, and shows significant computation speed during solution inference.

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ICLR Conference 2021 Conference Paper

Learnable Embedding sizes for Recommender Systems

Siyi Liu
Chen Gao 0001
Yihong Chen
Depeng Jin
Yong Li 0008

The embedding-based representation learning is commonly used in deep learning recommendation models to map the raw sparse features to dense vectors. The traditional embedding manner that assigns a uniform size to all features has two issues. First, the numerous features inevitably lead to a gigantic embedding table that causes a high memory usage cost. Second, it is likely to cause the over-fitting problem for those features that do not require too large representation capacity. Existing works that try to address the problem always cause a significant drop in recommendation performance or suffers from the limitation of unaffordable training time cost. In this paper, we proposed a novel approach, named PEP (short for Plug-in Embedding Pruning), to reduce the size of the embedding table while avoiding the drop of recommendation accuracy. PEP prunes embedding parameter where the pruning threshold(s) can be adaptively learned from data. Therefore we can automatically obtain a mixed-dimension embedding-scheme by pruning redundant parameters for each feature. PEP is a general framework that can plug in various base recommendation models. Extensive experiments demonstrate it can efficiently cut down embedding parameters and boost the base model's performance. Specifically, it achieves strong recommendation performance while reducing 97-99% parameters. As for the computation cost, PEP only brings an additional 20-30% time cost compare with base models.

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TIST Journal 2021 Journal Article

Linking Multiple User Identities of Multiple Services from Massive Mobility Traces

Huandong Wang
Yong Li
Gang Wang
Depeng Jin

Understanding the linkability of online user identifiers (IDs) is critical to both service providers (for business intelligence) and individual users (for assessing privacy risks). Existing methods are designed to match IDs across two services but face key challenges of matching multiple services in practice, particularly when users have multiple IDs per service. In this article, we propose a novel system to link IDs across multiple services by exploring the spatial-temporal features of user activities, of which the core idea is that the same user's online IDs are more likely to repeatedly appear at the same location. Specifically, we first utilize a contact graph to capture the “co-location” of all IDs across multiple services. Based on this graph, we propose a set-wise matching algorithm to discover candidate ID sets and use Bayesian inference to generate confidence scores for candidate ranking, which is proved to be optimal. We evaluate our system using two real-world ground-truth datasets from an Internet service provider (4 services, 815K IDs) and Twitter-Foursquare (2 services, 770 IDs). Extensive results show that our system significantly outperforms the state-of-the-art algorithms in accuracy (AUC is higher by 0.1–0.2), and it is highly robust against data quality, matching order, and number of services.

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NeurIPS Conference 2021 Conference Paper

Progressive Feature Interaction Search for Deep Sparse Network

Chen Gao
Yinfeng Li
Quanming Yao
Depeng Jin
Yong Li

Deep sparse networks (DSNs), of which the crux is exploring the high-order feature interactions, have become the state-of-the-art on the prediction task with high-sparsity features. However, these models suffer from low computation efficiency, including large model size and slow model inference, which largely limits these models' application value. In this work, we approach this problem with neural architecture search by automatically searching the critical component in DSNs, the feature-interaction layer. We propose a distilled search space to cover the desired architectures with fewer parameters. We then develop a progressive search algorithm for efficient search on the space and well capture the order-priority property in sparse prediction tasks. Experiments on three real-world benchmark datasets show promising results of PROFIT in both accuracy and efficiency. Further studies validate the feasibility of our designed search space and search algorithm.

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TIST Journal 2020 Journal Article

DeepApp

Tong Xia
Yong Li
Jie Feng
Depeng Jin
Qing Zhang
Hengliang Luo
Qingmin Liao

Smartphone mobile application (App) usage prediction, i.e., which Apps will be used next, is beneficial for user experience improvement. Through an in-depth analysis on a real-world dataset, we find that App usage is highly spatio-temporally correlated and personalized. Given the ability to model complex spatio-temporal contexts, we aim to apply deep learning to achieve high prediction accuracy. However, the personalization yields a problem: training one network for each individual suffers from data scarcity, yet training one deep neural network for all users often fails to uncover user preference. In this article, we propose a novel App usage prediction framework, named DeepApp, to achieve context-aware prediction via multi-task learning. To tackle the challenge of data scarcity, we train one general network for multiple users to share common patterns. To better utilize the spatio-temporal contexts, we supplement a location prediction task in the multi-task learning framework to learn spatio-temporal relations. As for the personalization, we add a user identification task to capture user preference. We evaluate DeepApp on the large-scale dataset by extensive experiments. Results demonstrate that DeepApp outperforms the start-of-the-art baseline by 6.44%.

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NeurIPS Conference 2020 Conference Paper

Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering

Jingtao Ding
Yuhan Quan
Quanming Yao
Yong Li
Depeng Jin

Negative sampling approaches are prevalent in implicit collaborative ﬁltering for obtaining negative labels from massive unlabeled data. As two major concerns in negative sampling, efﬁciency and effectiveness are still not fully achieved by recent works that use complicate structures and overlook risk of false negative instances. In this paper, we ﬁrst provide a novel understanding of negative instances by empirically observing that only a few instances are potentially important for model learning, and false negatives tend to have stable predictions over many training iterations. Above ﬁndings motivate us to simplify the model by sampling from designed memory that only stores a few important candidates and, more importantly, tackle the untouched false negative problem by favouring high-variance samples stored in memory, which achieves efﬁcient sampling of true negatives with high-quality. Empirical results on two synthetic datasets and three real-world datasets demonstrate both robustness and superiorities of our negative sampling method. The implementation is available at https: //github. com/dingjingtao/SRNS.

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IJCAI Conference 2019 Conference Paper

DeepAPF: Deep Attentive Probabilistic Factorization for Multi-site Video Recommendation

Huan Yan
Xiangning Chen
Chen Gao
Yong Li
Depeng Jin

Existing web video systems recommend videos according to users' viewing history from its own website. However, since many users watch videos in multiple websites, this approach fails to capture these users' interests across sites. In this paper, we investigate the user viewing behavior in multiple sites based on a large scale real dataset. We find that user interests are comprised of cross-site consistent part and site-specific part with different degrees of the importance. Existing linear matrix factorization recommendation model has limitation in modeling such complicated interactions. Thus, we propose a model of Deep Attentive Probabilistic Factorization (DeepAPF) to exploit deep learning method to approximate such complex user-video interaction. DeepAPF captures both cross-site common interests and site-specific interests with non-uniform importance weights learned by the attentional network. Extensive experiments show that our proposed model outperforms by 17. 62%, 7. 9% and 8. 1% with the comparison of three state-of-the-art baselines. Our study provides insight to integrate user viewing records from multiple sites via the trusted third party, which gains mutual benefits in video recommendation.

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AAAI Conference 2019 Conference Paper

DeepSTN+: Context-Aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis

Ziqian Lin
Jie Feng
Ziyang Lu
Yong Li
Depeng Jin

Crowd flow prediction is of great importance in a wide range of applications from urban planning, traffic control to public safety. It aims to predict the inflow (the traffic of crowds entering a region in a given time interval) and outflow (the traffic of crowds leaving a region for other places) of each region in the city with knowing the historical flow data. In this paper, we propose DeepSTN+, a deep learning-based convolutional model, to predict crowd flows in the metropolis. First, Deep- STN+ employs the ConvPlus structure to model the longrange spatial dependence among crowd flows in different regions. Further, PoI distributions and time factor are combined to express the effect of location attributes to introduce prior knowledge of the crowd movements. Finally, we propose an effective fusion mechanism to stabilize the training process, which further improves the performance. Extensive experimental results based on two real-life datasets demonstrate the superiority of our model, i. e. , DeepSTN+ reduces the error of the crowd flow prediction by approximately 8%∼13% compared with the state-of-the-art baselines.

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IJCAI Conference 2019 Conference Paper

Reinforced Negative Sampling for Recommendation with Exposure Data

Jingtao Ding
Yuhan Quan
Xiangnan He
Yong Li
Depeng Jin

In implicit feedback-based recommender systems, user exposure data, which record whether or not a recommended item has been interacted by a user, provide an important clue on selecting negative training samples. In this work, we improve the negative sampler by integrating the exposure data. We propose to generate high-quality negative instances by adversarial training to favour the difﬁcult instances, and by optimizing additional objective to favour the real negatives in exposure data. However, this idea is non-trivial to implement since the distribution of exposure data is latent and the item space is discrete. To this end, we design a novel RNS method (short for Reinforced Negative Sampler) that generates exposure-alike negative instances through feature matching technique instead of directly choosing from exposure data. Optimized under the reinforcement learning framework, RNS is able to integrate user preference signals in exposure data and hard negatives. Extensive experiments on two real-world datasets demonstrate the effectiveness and rationality of our RNS method. Our implementation is available at: https: //github. com/dingjingtao/ReinforceNS.

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IJCAI Conference 2018 Conference Paper

Improving Implicit Recommender Systems with View Data

Jingtao Ding
Guanghui Yu
Xiangnan He
Yuhan Quan
Yong Li
Tat-Seng Chua
Depeng Jin
Jiajie Yu

Most existing recommender systems leverage the primary feedback data only, such as the purchase records in E-commerce. In this work, we additionally integrate view data into implicit feedback based recommender systems (dubbed as Implicit Recommender Systems). We propose to model the pairwise ranking relations among purchased, viewed, and non-viewed interactions, being more effective and ﬂexible than typical pointwise matrix factorization (MF) methods. However, such a pairwise formulation poses efﬁciency challenges in learning the model. To address this problem, we design a new learning algorithm based on the element-wise Alternating Least Squares (eALS) learner. Notably, our algorithm can efﬁciently learn model parameters from the whole user-item matrix (including all missing data), with a rather low time complexity that is dependent on the observed data only. Extensive experiments on two real-world datasets demonstrate that our method outperforms several state-of-the-art MF methods by 10% ∼ 28. 4%. Our implementation is available at: https: //github. com/ dingjingtao/View_enhanced_ALS.

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