Trend-responsive User Segmentation Enabling Traceable Publishing Insights. A Case Study of a Real-world Large-scale News Recommendation System

TL;DR

This paper introduces a trend-responsive, unsupervised user segmentation method integrated into a large-scale news recommendation system, significantly improving personalization and enabling traceable publishing insights through online experiments and trend analysis.

Contribution

It presents a novel unsupervised, trend-aware user segmentation approach tailored for large-scale news recommender systems, enhancing recommendation quality and content diversity.

Findings

Significant improvements in recommendation performance in online A/B tests

Effective segmentation capturing user interests and trend dynamics

Enhanced traceability for content publishing insights

Abstract

The traditional offline approaches are no longer sufficient for building modern recommender systems in domains such as online news services, mainly due to the high dynamics of environment changes and necessity to operate on a large scale with high data sparsity. The ability to balance exploration with exploitation makes the multi-armed bandits an efficient alternative to the conventional methods, and a robust user segmentation plays a crucial role in providing the context for such online recommendation algorithms. In this work, we present an unsupervised and trend-responsive method for segmenting users according to their semantic interests, which has been integrated with a real-world system for large-scale news recommendations. The results of an online A/B test show significant improvements compared to a global-optimization algorithm on several services with different characteristics. Based on the experimental results as well as the exploration of segments descriptions and trend dynamics, we propose extensions to this approach that address particular real-world challenges for different use-cases. Moreover, we describe a method of generating traceable publishing insights facilitating the creation of content that serves the diversity of all users needs.