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

TL;DR

DeepCP introduces a deep learning framework for privacy-preserving content placement in closed social networks, accurately predicting content popularity and optimizing placement to reduce latency and improve user experience.

Contribution

The paper presents a novel deep learning-based approach combining cascade prediction and GAN-driven placement for closed social networks, without relying on personal user data.

Findings

High accuracy in content popularity prediction

Significant reduction in content access latency

Effective autonomous content placement in real-time

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.