Session-based News Recommendation Using Cohesive Patterns

In the rapidly evolving field of news recommendation, where user preferences are highly dynamic and content quickly becomes obsolete, providing timely and relevant recommendations presents a significant challenge. Traditional recommender systems typically rely on complex collaborative filtering models that depend on extensive user histories. In the news domain, however, such histories are often scarce due to the high prevalence of anonymous users. To address these challenges, we introduce a novel session-based recommendation method that leverages cohesive sequential pattern mining. Rather than relying on traditional frequency-based pattern utility metrics, our approach prioritizes pattern cohesiveness, which captures the temporal proximity of item interactions within a pattern, resulting in recommendations that align more closely with the user’s ongoing session.We conduct a comprehensive empirical evaluation of our approach using four large-scale real-world news datasets. The results demonstrate that our method, SeQcsp, significantly outperforms state-of-the-art session-based recommendation algorithms in terms of accuracy, ranking quality, as well as diversity. Furthermore, SeQcsp provides recommendations faster than most existing methods and is effective for both short and long user sessions, highlighting its robustness, adaptability, and efficiency.