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Explore user neighborhood for real-time e-commerce recommendation

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arXiv 2021年

作者： Xie, Xu Sun, Fei Yang, Xiaoyong Yang, Zhao Gao, Jinyang Ou, Wenwu Cui, Bin Peking University China Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications China Institute of Computational Social Science Peiking University Qingdao China Alibaba Group China

Recommender systems play a vital role in modern online services, such as Amazon and Taobao. Traditional personalized methods, which focus on user-item (UI) relations, have been widely applied in industrial settings, owing to their efficiency and effectiveness. Despite their success, we argue that these approaches ignore local information hidden in similar users. To tackle this problem, user-based methods exploit similar user relations to make recommendations in a local perspective. Nevertheless, traditional user-based methods, like userKNN and matrix factorization, are intractable to be deployed in the real-time applications since such transductive models have to be recomputed or retrained with any new interaction. To overcome this challenge, we propose a framework called self-complementary collaborative filtering (SCCF) which can make recommendations with both global and local information in real time. On the one hand, it utilizes UI relations and user neighborhood to capture both global and local information. On the other hand, it can identify similar users for each user in real time by inferring user representations on the fly with an inductive model. The proposed framework can be seamlessly incorporated into existing inductive UI approach and benefit from user neighborhood with little additional computation. It is also the first attempt to apply user-based methods in real-time settings. The effectiveness and efficiency of SCCF are demonstrated through extensive offline experiments on four public datasets, as well as a large scale online A/B test in Taobao. © 2021, CC BY-SA.

关键词： Collaborative filtering

Efficient Diversity-Driven Ensemble for Deep Neural Networks

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arXiv 2021年

作者： Zhang, Wentao Jiang, Jiawei Shao, Yingxia Cui, Bin Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications China Peking University China ETH Zurich Switzerland School of Computer Science Beijing University of Posts and Telecommunications China

The ensemble of deep neural networks has been shown, both theoretically and empirically, to improve generalization accuracy on the unseen test set. However, the high training cost hinders its efficiency since we need a sufficient number of base models and each one in the ensemble has to be separately trained. Lots of methods are proposed to tackle this problem, and most of them are based on the feature that a pre-trained network can transfer its knowledge to the next base model and then accelerate the training process. However, these methods suffer a severe problem that all of them transfer knowledge without selection and thus lead to low diversity. As the effect of ensemble learning is more pronounced if ensemble members are accurate and diverse, we propose a method named Efficient Diversity-Driven Ensemble (EDDE) to address both the diversity and the efficiency of an ensemble. To accelerate the training process, we propose a novel knowledge transfer method which can selectively transfer the previous generic knowledge. To enhance diversity, we first propose a new diversity measure, then use it to define a diversity-driven loss function for optimization. At last, we adopt a Boosting-based framework to combine the above operations, such a method can also further improve diversity. We evaluate EDDE on Computer Vision (CV) and Natural Language Processing (NLP) tasks. Compared with other well-known ensemble methods, EDDE can get highest ensemble accuracy with the lowest training cost, which means it is efficient in the ensemble of neural networks. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

EarthquakeAware: Visual Analytics for Understanding Human Impacts of Earthquakes from Social Media data

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EarthquakeAware: Visual Analytics for Understanding Human Im...

IEEE Symposium on Visual Analytics Science and Technology (VAST)

作者： Shuai Chen Sihang Li Liwenhan Xie Yi Zhong Yun Han Xiaoru Yuan Key Laboratory of Machine Perception Ministry of Education School of EECS Peking University National Engineering Laboratory for Big Data Analysis and Application Peking University

In this paper, we propose EathquakeAware, a visual analytics system for social media visualization. Our system supports analyzing geo-tagged messages from different perspectives, including location, keyword, account. It can summarize the evolution patterns of topics and reveal the discussing trend of different keywords to help evaluate situations in different regions. We take VAST Challenge 2019 Mini Challenge 3 as case studies to demonstrate its effectiveness in a fictitious analysis scenario.

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DRL360: 360-degree Video Streaming with Deep Reinforcement Learning

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DRL360: 360-degree Video Streaming with Deep Reinforcement L...

IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Yuanxing Zhang Pengyu Zhao Kaigui Bian Yunxin Liu Lingyang Song Xiaoming Li School of Electronics Engineering and Computer Science Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Beijing China Microsoft Research Beijing China

360-degree videos have gained more popularity in recent years, owing to the great advance of panoramic cameras and head-mounted devices. However, as 360-degree videos are usually in high resolution, transmitting the content requires extremely high bandwidth. To protect the Quality of Experience (QoE) of users, researchers have proposed tile-based 360-degree video streaming systems that allocate high/low bit rates to selected tiles of video frames for streaming over the limited bandwidth. It is challenging to determine which tiles should be allocated with a high/low rate, because (1) the video playbacks include too many features that dynamically change over time when making the rate allocation; (2) most of the state-of-the-art systems focus on a fixed set of heuristics to optimize a specific QoE objective, while users may have various QoE objectives that need to be optimized in different ways. This paper presents a Deep Reinforcement Learning (DRL) based framework for 360-degree video streaming, named DRL360. The DRL360 framework helps improve the system performance by jointly optimizing multiple QoE objectives across a broad set of dynamic features. The DRL-based model adaptively allocates rates for the tiles of the future video frames based on the observations collected by client video players. We compare the proposed DRL360 to the existing systems by trace-driven evaluations as well as conducting a realworld experiment over a wide variety of network conditions. Evaluation results reveal that DRL360 can adapt to all considered scenarios, and outperform the state-of-the-art approaches by 20%-30% on average given different QoE objectives.

关键词： Streaming media Quality of experience Resource management Bandwidth Adaptation models Reinforcement learning Bit rate

Carbon-neutral pathway to mitigating transport-power grid cross-sector effects

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The Innovation 2024年第3期5卷 27-29页

作者： Jing Ma Huiwen Kong Jianxiao Wang Haiwang Zhong Bo Li Jie Song Daniel M.Kammen State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Electrical and Electronic EngineeringNorth China Electric Power UniversityBeijing 102206China National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijing 100871China Peking University Ordos Research Institute of Energy Ordos 017000China State Key Laboratory of Power Systems and Generation Equipment Department of Electrical EngineeringTsinghua UniversityBeijing 100084China School of Electrical Engineering Guangxi UniversityNanning 530004China Department of Industrial Engineering and Management College of EngineeringPeking UniversityBeijing 100871China Energy and Resources Group and Goldman School of Public PolicyUniversity of CaliforniaBerkeleyBerkeleyCA 94720USA

Dear Editor,The growing demand for transportation energy has brought increasing challenges to reducing greenhouse gas ***,many countries or regions have proposed solutions to achieve carbon-neutral transportation such as the rapid expansion of the global electric vehicle(EV)***,these benefits are not *** the goal of decarbonization in electricity is achieved,the“pseudo net zero emissions”effect of the transportation sector will inevitably be accompanied by a quiet shifting of carbon responsibility.1 This shift is undoubtedly fatal for both net zero emissions at the national level and carbon responsibility at the industry level;however,the corresponding effects have not yet been clarified.

关键词： neutral doubt shifting

An efficient fifth-order interpolation-based Hermite WENO scheme for hyperbolic conservation laws

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arXiv 2024年

作者： Xie, Xiaoyang Tao, Zhanjing Jiao, Chunhai Zhang, Min College of Engineering Peking University Beijing100871 China School of Mathematics Jilin University Changchun130012 China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing100871 China Chongqing Research Institute of Big Data Peking University Chongqing401121 China

In this paper, we develop a simple, efficient, and fifth-order finite difference interpolation-based Hermite WENO (HWENO-I) scheme for one- and two-dimensional hyperbolic conservation laws. We directly interpolate the solution and first-order derivative values and evaluate the numerical fluxes based on these interpolated values. We do not need the split of the flux functions when reconstructing numerical fluxes and there is no need for any additional HWENO interpolation for the modified derivative. The HWENO interpolation only needs to be applied one time which utilizes the same candidate stencils, Hermite interpolation polynomials, and linear/nonlinear weights for the solution and first-order derivative at the cell interface, as well as the modified derivative at the cell center. The HWENO-I scheme inherits the advantages of the finite difference flux-reconstruction-based HWENO-R scheme [Fan et al., Comput. Methods Appl. Mech. Engrg., 2023], including fifth-order accuracy, compact stencils, arbitrary positive linear weights, and high resolution. The HWENO-I scheme is simpler and more efficient than the HWENO-R scheme and the previous finite difference interpolation-based HWENO scheme [Liu and Qiu, J. Sci. Comput., 2016] which needs the split of flux functions for the stability and upwind performance for the high-order derivative terms. Various benchmark numerical examples are presented to demonstrate the accuracy, efficiency, high resolution, and robustness of the proposed HWENO-I scheme. Copyright © 2024, The Authors. All rights reserved.

关键词： Interpolation

Optimal Energy Dispatch of Grid-Connected Electric Vehicle Considering Lithium Battery Electrochemical Model

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arXiv 2024年

作者： Chen, Yuanbo Zheng, Kedi Gu, Yuxuan Wang, Jianxiao Chen, Qixin State Key Laboratory of Power Systems Department of Electrical Engineering Tsinghua University Beijing100084 China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing100871 China

The grid-connected electric vehicles (EVs) serve as a promising regulating resource in the distribution grid with Vehicle-to-Grid (V2G) facilities. In the day-ahead stage, electric vehicle batteries (EVBs) need to be precisely dispatched and controlled to ensure high efficiency and prevent degradation. This article focuses on considering a refined battery model, i.e. the electrochemical model (EM), in the optimal dispatch of the local energy system with high penetration of EVs which replenish energy through V2G-equipped charge station and battery swapping station (BSS). In this paper, to utilize the EM efficiently, recursive EVB constraints and a corresponding matrix-based state update method are proposed based on EM power characterization. The charging EV state distribution is profiled and a multi-layer BSS model along with binary aggregation is proposed, in order to overcome the computation complexity of combining the refined battery constraints with the mixed integer optimization. Finally, a local energy system scenario is investigated for evaluation. The efficiency and effectiveness of EM consideration are assessed from the perspective of both the system and battery. Copyright © 2024, The Authors. All rights reserved.

关键词： Charging stations

PISketch: Finding Persistent and Infrequent Flows 3

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PISketch: Finding Persistent and Infrequent Flows

3rd ACM SIGCOMM 2022 Workshop on Formal Foundations and Security of Programmable Network Infrastructures, FFSPIN 2022

作者： Fan, Zhuochen Hu, Zhoujing Wu, Yuhan Guo, Jiarui Liu, Wenrui Yang, Tong Wang, Hengrui Xu, Yifei Uhlig, Steve Tu, Yaofeng School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Peking University China Computer Science Department University of California Los Angeles United States School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom ZTE Corporation China

ISBN: (纸本)9781450393294

Finding persistent and inactive activity periods is very helpful in practice, for example to detect intrusion activities. Most of the literature focuses on finding persistent flows or frequent flows. No previous work is able to find persistent and infrequent flows. In this paper, we propose a novel sketch data structure, PISketch, to find persistent and infrequent flows in real time. The key idea of PISketch is to define a weight and its Reward and Penalty System for each flow to combine and balance the information of both persistency and infrequency, and to keep high-weighted flows in a limited space through a strategy. We implement PISketch on P4 and CPU platforms, and compare the performance of PISketch with two strawman solutions (On-Off + CM sketch, and PIE + CM sketch), in terms of finding persistent and infrequent flows. Our experimental results demonstrate the advantage of PISketch, by comparing it to two strawman solutions: 1) The F1 Score of PISketch is around 22.1% and 57.6% higher than two strawman solutions, respectively;2) The Average Relative Error (ARE) of PISketch is around 820.9 (up to 1188.8) and 126.2 (up to 265.6) times lower than two strawman solutions, respectively;3) The insertion throughput of PISketch is around 1.23 and 16.5 times higher than two strawman solutions, respectively. Moreover, we implement two concrete cases of PISketch through end-to-end experiments. All of our codes are available at GitHub. © 2022 ACM.

关键词： infrequent items persistent items sketch weight fusion

Resilience-enhanced Power System Dispatch Amidst Extreme Blizzard Events

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Resilience-enhanced Power System Dispatch Amidst Extreme Bli...

Power Science and Technology (ICPST), IEEE International Conference on

作者： Kuo Wang Jianxiao Wang Yiyang Song College of Engineering Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong SAR China

ISBN: (数字)9798350349030

ISBN: (纸本)9798350349047

Enhancing the resilience of power systems against escalating threats from extreme blizzard events has become increasingly vital. Despite advances in research, there is a notable gap in modeling the mapping relationship between blizzard scenarios and the failure rates of power system components, alongside developing corresponding emergency dispatch strategies. To address this, our work presents a novel emergency dispatch strategy underpinned by a two-stage stochastic optimization model. This model integrates real-time weather forecasting and advanced stochastic methods to optimize the balance between electricity generation costs and the economic impacts of unserved energy during blizzard conditions, emphasizing the innovative mapping of blizzard scenarios to component failure rates. Employing the IEEE 30-bus test system demonstrates the strategy’s effectiveness in enhancing operational stability and reducing socioeconomic impacts, suggesting its potential for broad adoption in improving system resilience to extreme weather.

关键词： Fault tolerance Adaptation models Costs Stochastic processes Weather forecasting Power system stability Stability analysis