1 research outputs found
DeepCP: Deep Learning Driven Cascade Prediction Based Autonomous Content Placement in Closed Social Network
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.Comment: accepted by IEEE Journal on Selected Areas in Communications (JSAC),
March 202