Shaping the Future of Content-based News Recommenders: Insights from Evaluating Feature-Specific Similarity Metrics

Abstract

In news media, recommender system technology faces several domain-specific challenges. The continuous stream of new content and users deems content-based recommendation strategies, based on similar-item retrieval, to remain popular. However, a persistent challenge is to select relevant features and corresponding similarity functions, and whether this depends on the specific context. We evaluated feature-specific similarity metrics using human similarity judgments across national and local news domains. We performed an online experiment (N = 141) where we asked participants to judge the similarity between pairs of randomly sampled news articles. We had three contributions: (1) comparing novel metrics based on large language models to ones traditionally used in news recommendations, (2) exploring differences in similarity judgments across national and local news domains, and (3) examining which content-based strategies were perceived as appropriate in the news domain. Our results showed that one of the novel large language model based metrics (SBERT) was highly correlated with human judgments, while there were only small, most non-significant differences across national and local news domains. Finally, we found that while it may be possible to automatically recommend similar news using feature-specific metrics, their representativeness and appropriateness varied. We explain how our findings can guide the design of future content-based and hybrid recommender strategies in the news domain.