Algorithms and System Architecture for Immediate Personalized News Recommendations

TL;DR

This paper presents algorithms and system architecture for immediate personalized news recommendations that adapt quickly to changing news trends and user interests, demonstrated through offline and online evaluations.

Contribution

It introduces a practical system architecture and algorithms for real-time news personalization, focusing on immediacy and scalability in production environments.

Findings

Algorithms effectively adapt to rapid news trend changes.

System architecture supports large-scale online deployment.

Offline and online tests confirm improved recommendation relevance.

Abstract

Personalization plays an important role in many services, just as news does. Many studies have examined news personalization algorithms, but few have considered practical environments. This paper provides algorithms and system architecture for generating immediate personalized news in a practical environment. Immediacy means changes in news trends and user interests are reflected in recommended news lists quickly. Since news trends and user interests rapidly change, immediacy is critical in news personalization applications. We develop algorithms and system architecture to realize immediacy. Our algorithms are based on collaborative filtering of user clusters and evaluate news articles using click-through rate and decay scores based on the time elapsed since the user's last access. Existing studies have not fully discussed system architecture, so a major contribution of this paper is that we demonstrate a system architecture and realize our algorithms and a configuration example implemented on top of Amazon Web Services. We evaluate the proposed method both offline and online. The offline experiments are conducted through a real-world dataset from a commercial news delivery service, and online experiments are conducted via A/B testing on production environments. We confirm the effectiveness of our proposed method and also that our system architecture can operate in large-scale production environments.