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.

本文介绍了一种基于深度学习的数据驱动框架 DeepCP，通过预测内容流行度和传播模式，提出了自主内容放置机制 CP-GAN 以降低内容访问延迟和增强用户体验，以在微信朋友圈中实现有效的内容放置。实验表明 DeepCP 能够高精度地预测内容流行度，快速生成有效的内容放置决策。