Content Popularity

Content popularity prediction focuses on accurately forecasting the future success of online content, crucial for applications like recommendation systems and resource allocation. Current research emphasizes improving prediction accuracy by incorporating dynamic factors like temporal trends and user behavior, often employing advanced machine learning models such as diffusion models, Bayesian methods, and neural networks within federated learning frameworks to handle large-scale, distributed data. These advancements aim to overcome limitations of existing methods, particularly in addressing popularity bias and handling shifts in data distributions, ultimately leading to more robust and reliable predictions. The improved accuracy and efficiency of these models have significant implications for optimizing online platforms and enhancing user experience.

Papers

October 12, 2024

Contrastive Learning for Implicit Social Factors in Social Media Popularity Prediction
Zhizhen Zhang, Ruihong Qiu, Xiaohui Xie
Contrastive Learning Social Medium Popularity Prediction Social Factor User Similarity Content Popularity

September 25, 2024

CasFT: Future Trend Modeling for Information Popularity Prediction with Dynamic Cues-Driven Diffusion Models
Xin Jing, Yichen Jing, Yuhuan Lu, Bangchao Deng, Xueqin Chen, Dingqi Yang
Diffusion Model Future Trend Event Forecasting Information Cascade Content Popularity

June 17, 2024

Explainable assessment of financial experts' credibility by classifying social media forecasts and checking the predictions with actual market data
Silvia García-Méndez, Francisco de Arriba-Pérez, Jaime González-Gonzáleza, Francisco J. González-Castaño
Human Prediction Credibility Assessment Content Popularity

June 5, 2024

What Drives Online Popularity: Author, Content or Sharers? Estimating Spread Dynamics with Bayesian Mixture Hawkes
Pio Calderon, Marian-Andrei Rizoiu
Hawkes Process Sharing Matter Information Spread Content Popularity Spread Model

October 16, 2023

Robust Collaborative Filtering to Popularity Distribution Shift
An Zhang, Wenchang Ma, Jingnan Zheng, Xiang Wang, Tat-seng Chua
Popularity Bias Popularity Score Robust Recommendation Dynamic Content Popularity Content Popularity

July 2, 2022

Unsupervised Recurrent Federated Learning for Edge Popularity Prediction in Privacy-Preserving Mobile Edge Computing Networks
Chong Zheng, Shengheng Liu, Yongming Huang, Wei Zhang, Luxi Yang
Mobile Edge Computing Edge Network Mobile Edge Popularity Prediction Unsupervised Federated Learning Dynamic Content Popularity Content Popularity

June 23, 2022

Content Popularity Prediction Based on Quantized Federated Bayesian Learning in Fog Radio Access Networks
Yunwei Tao, Yanxiang Jiang, Fu-Chun Zheng, Pengcheng Zhu, Dusit Niyato, Xiaohu You
Bayesian Learning Popularity Prediction Content Popularity

June 13, 2022

Content Popularity Prediction in Fog-RANs: A Clustered Federated Learning Based Approach
Zhiheng Wang, Yanxiang Jiang, Fu-Chun Zheng, Mehdi Bennis, Xiaohu You
Clustered Federated Learning Popularity Prediction Content Popularity

Content Popularity

Papers

Contrastive Learning for Implicit Social Factors in Social Media Popularity Prediction

CasFT: Future Trend Modeling for Information Popularity Prediction with Dynamic Cues-Driven Diffusion Models

Explainable assessment of financial experts' credibility by classifying social media forecasts and checking the predictions with actual market data

What Drives Online Popularity: Author, Content or Sharers? Estimating Spread Dynamics with Bayesian Mixture Hawkes

Robust Collaborative Filtering to Popularity Distribution Shift

Unsupervised Recurrent Federated Learning for Edge Popularity Prediction in Privacy-Preserving Mobile Edge Computing Networks

Content Popularity Prediction Based on Quantized Federated Bayesian Learning in Fog Radio Access Networks

Content Popularity Prediction in Fog-RANs: A Clustered Federated Learning Based Approach