A Look Into News Avoidance Through AWRS: An Avoidance-Aware Recommender System
This work addresses the issue of news avoidance for journalists and users by proposing an incremental improvement to recommender systems.
The paper tackled the problem of news article avoidance in recommender systems by introducing AWRS, an avoidance-aware framework that incorporates avoidance as a key factor alongside exposure and relevance, and demonstrated its superiority over existing methods across three multilingual news datasets.
In recent years, journalists have expressed concerns about the increasing trend of news article avoidance, especially within specific domains. This issue has been exacerbated by the rise of recommender systems. Our research indicates that recommender systems should consider avoidance as a fundamental factor. We argue that news articles can be characterized by three principal elements: exposure, relevance, and avoidance, all of which are closely interconnected. To address these challenges, we introduce AWRS, an Avoidance-Aware Recommender System. This framework incorporates avoidance awareness when recommending news, based on the premise that news article avoidance conveys significant information about user preferences. Evaluation results on three news datasets in different languages (English, Norwegian, and Japanese) demonstrate that our method outperforms existing approaches.