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基于深度学习的推荐系统研究综述 - ict.ac.cn

recommendation method, collaborative filtering and hybrid recommendation method, and then we give an overview of the main deep learning techniques and briefly introduce their applications in the recommender systems. And secondly, we provide a comprehensive summary of current research on deep learning based

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数据中心网络的流量控制：研究现状与趋势

cjc.ict.ac.cn

DCTCP, and the most suitable traffic control algorithm for RDMA data center is DCQCN. Other researches require expensive custom hardware, which is difficult to deploy. (2) The traffic control technology is a technology of fair utilization of limited resources. Therefore, the performance of the technology can be improved

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区块链技术：架构及进展 - ict.ac.cn

cjc.ict.ac.cn

blockchain scalability is analyzed about sharding and multichannel, blockchain security is discussed from digital signing and verification, and privacy preserving. This paper also analyzes the advantages, disadvantages and technology trends of blockchain by comparing with traditional databases, and gives several challenging research

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车联网边缘计算环境下基于深度强化学习的分布 式服务卸载方法

cjc.ict.ac.cn

A Deep Reinforcement Learning-BasedDistributed Service Offloading Method ... average service latency by 0.4% to 20.4% compared with four exiting service offloading methods in different IoV environments, proving the effectiveness and efficiency of D-SOAC. ... asynchronous advantage actor-critic 1 引言 ...

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深度强化学习综述 - ict.ac.cn

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optimization methods to optimize the policies. In this part, we firstly highlight some pure policy gradient methods, then focus on a series of policy-based DRL algorithms which use the actor-critic framework e.g., Deep Deterministic Policy Gradient (DDPG), followed by an effective method named Asynchronous Advantage

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卷积神经网络研究综述 - ict.ac.cn

cjc.ict.ac.cn

AlphaGo 主要采用价值网络（value networks） 来评估棋盘的位置，用策略网络（policy networks） 来选择下棋步法，这两种网络都是深层神经网络模 型，AlphaGo 所取得的成果是深度学习带来的人工 智能的又一次突破，这也说明了深度学习具有强大 的潜力。

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生成对抗网络及其在图像生成中的 ... - ict.ac.cn

cjc.ict.ac.cn

and computational challenges. At the same time, generative adversarial networks are the latest and most successful technology among generative models. Especially in terms of image generation, compared with other generation models, generative adversarial networks can not only avoid complicated calculations, but also generate better quality images.

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脉冲神经网络研究现状及展望 - ict.ac.cn

cjc.ict.ac.cn

(e.g., dopamine-based reward learning, energy-based learning). Hence, it is powerful on spatially-temporal information representation, asynchronous processing of event-based information, and self-organized learning with dynamic topologies. SNN belongs to cross-discipline research areas of brain science and computer science.

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Item-Based Collaborative Filtering Recommendation …

files.grouplens.org

Item-Based Collaborative Filtering Recommendation Algorithms Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl !#"$&% ' ( )* ' (GroupLens Research Group/Army HPC Research Center Department of Computer Science and Engineering

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Industry Report Amazon.com Recommendations - UMD

www.cs.umd.edu

tive filtering, cluster models, and search-based methods. Here, we compare these methods with our algorithm, which we call item-to-item collab-orative filtering. Unlike traditional collaborative filtering, our algorithm’s online computation scales independently of the number of customers and number of items in the product catalog. Our algo-

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COVER FEATURE MATRIX FACTORIZATION ... - DataJobs.com

datajobs.com

An alternative to content filtering relies only on past user behavior—for example, previous transactions or product ratings— without requiring the creation of explicit profiles. This approach is known as col-laborative filtering, a term coined by the developers of Tapestry, the first recom-mender system.1 Collaborative filtering

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arXiv:1511.06939v4 [cs.LG] 29 Mar 2016

arxiv.org

shown to be one of the best performing Collaborative Filtering models. Deep Models have been used to extract features from unstructured content such as music or images that are then used together with more conventional collaborative filtering models. In Van den Oord et al. (2013) a convolutional deep

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Recommendation Systems - Stanford University

infolab.stanford.edu

Collaborative-Filtering systems focus on the relationship between users and items. Similarity of items is determined by the similarity of the ratings of those items by the users who have rated both items. In this section, we focus on content-based recommendation systems. The …

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The role of public service and public ... - United Nations

www.un.org

collaborative, and competent in the use of technology all ... As information about the virus started filtering through, public health officials, the news media, and re-

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