Improving Latent User Models in Online Social Media

TL;DR

This paper introduces a mutual-enhancement framework that improves latent user models in social media by effectively handling data sparsity and discovering rare behaviors, leading to significant performance gains.

Contribution

It presents a novel user partitioning approach and a scalable, parallelizable framework that enhances behavior modeling in complex, dynamic social environments.

Findings

Achieves 15% average improvement over state-of-the-art models

Significantly benefits users with limited interaction data

Framework scales linearly and is suitable for large datasets

Abstract

Modern social platforms are characterized by the presence of rich user-behavior data associated with the publication, sharing and consumption of textual content. Users interact with content and with each other in a complex and dynamic social environment while simultaneously evolving over time. In order to effectively characterize users and predict their future behavior in such a setting, it is necessary to overcome several challenges. Content heterogeneity and temporal inconsistency of behavior data result in severe sparsity at the user level. In this paper, we propose a novel mutual-enhancement framework to simultaneously partition and learn latent activity profiles of users. We propose a flexible user partitioning approach to effectively discover rare behaviors and tackle user-level sparsity. We extensively evaluate the proposed framework on massive datasets from real-world platforms including Q&A networks and interactive online courses (MOOCs). Our results indicate significant gains over state-of-the-art behavior models ( 15% avg ) in a varied range of tasks and our gains are further magnified for users with limited interaction data. The proposed algorithms are amenable to parallelization, scale linearly in the size of datasets, and provide flexibility to model diverse facets of user behavior.