Leveraging Sequential Episode Mining for Session-Based News Recommendation

News recommender systems aim to help users find interesting and relevant news stories while mitigating information overload. Over the past few decades, various challenges have emerged in developing effective algorithms for real-world scenarios. These challenges include capturing evolving user preferences and addressing concept drift during reading sessions. Additionally, ensuring the freshness and timeliness of news content poses significant obstacles. To address these issues, we utilize an innovative sequential pattern mining approach known as Marbles to capture user behavior. Marbles leverages frequent episodes to generate a collection of association rules, where a frequent episode is a partially ordered pattern that occurs frequently in the input sequence. The recommendation process involves identifying relevant rules extracted from these patterns and weighting them. Subsequently, a heuristic procedure assesses candidate rules and generates a list of recommendations for users based on their most recent reading session. Notably, we conduct our evaluation in a streaming scenario, simulating real-world usage, where both our algorithm and baselines dynamically improve their models with each user click. Through our empirical evaluation in this streaming-based scenario, which closely models real-world usage, we demonstrate the applicability of the Marbles algorithm in session-based recommendation. Our proposed approach outperforms baseline algorithms on two real-world data sets, effectively addressing the challenges specific to the news domain.