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      DeepCP: Deep Learning Driven Cascade Prediction Based Autonomous Content Placement in Closed Social Network

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          Abstract

          Online social networks (OSNs) are emerging as the most popular mainstream platform for content cascade diffusion. In order to provide satisfactory quality of experience (QoE) for users in OSNs, much research dedicates to proactive content placement by using the propagation pattern, user's personal profiles and social relationships in open social network scenarios (e.g., Twitter and Weibo). In this paper, we take a new direction of popularity-aware content placement in a closed social network (e.g., WeChat Moment) where user's privacy is highly enhanced. We propose a novel data-driven holistic deep learning framework, namely DeepCP, for joint diffusion-aware cascade prediction and autonomous content placement without utilizing users' personal and social information. We first devise a time-window LSTM model for content popularity prediction and cascade geo-distribution estimation. Accordingly, we further propose a novel autonomous content placement mechanism CP-GAN which adopts the generative adversarial network (GAN) for agile placement decision making to reduce the content access latency and enhance users' QoE. We conduct extensive experiments using cascade diffusion traces in WeChat Moment (WM). Evaluation results corroborate that the proposed DeepCP framework can predict the content popularity with a high accuracy, generate efficient placement decision in a real-time manner, and achieve significant content access latency reduction over existing schemes.

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          Author and article information

          Journal
          09 March 2020
          Article
          2003.03971
          9dc07380-ab08-445f-bd61-cfe0cd00637b

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          accepted by IEEE Journal on Selected Areas in Communications (JSAC), March 2020
          cs.NI cs.AI cs.DC cs.HC cs.SI

          Social & Information networks,Artificial intelligence,Networking & Internet architecture,Human-computer-interaction

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