Is This News Still Interesting to You?: Lifetime-aware Interest Matching for News Recommendation

TL;DR

This paper introduces LIME, a lifetime-aware interest matching framework for news recommendation that models news age and interest persistence to improve personalization accuracy.

Contribution

It proposes novel lifetime-aware strategies to better capture news relevance over time, addressing underexplored temporal challenges in news recommendation.

Findings

LIME outperforms state-of-the-art methods on real datasets.

Lifetime-aware strategies improve recommendation accuracy.

Model-agnostic approaches enhance existing systems.

Abstract

Personalized news recommendation aims to deliver news articles aligned with users' interests, serving as a key solution to alleviate the problem of information overload on online news platforms. While prior work has improved interest matching through refined representations of news and users, the following time-related challenges remain underexplored: (C1) leveraging the age of clicked news to infer users' interest persistence, and (C2) modeling the varying lifetime of news across topics and users. To jointly address these challenges, we propose a novel Lifetime-aware Interest Matching framework for nEws recommendation, named LIME, which incorporates three key strategies: (1) User-Topic lifetime-aware age representation to capture the relative age of news with respect to a user-topic pair, (2) Candidate-aware lifetime attention for generating temporally aligned user representation, and (3) Freshness-guided interest refinement for prioritizing valid candidate news at prediction time. Extensive experiments on two real-world datasets demonstrate that LIME consistently outperforms a wide range of state-of-the-art news recommendation methods, and its model agnostic strategies significantly improve recommendation accuracy.