Topical Preference Trumps Other Features in News Recommendation: A Conjoint Analysis on a Representative Sample from Norway

Abstract

A variety of news articles features can be used to tailor news content. However, only a few studies have actually compared the relative importance of different features in predicting news reading behavior in the context of news recommender systems. This study reports the results of a conjoint experiment, where we examined the relative importance of seven features in predicting a user’s intention to read, including: topic headline (Abortion vs Meat Eating), reading time, recency, geographic distance, topical preference match, demographic similarity, and general popularity in a news recommender system. To ensure an externally valid result, the study was distributed among a representative Norwegian sample (𝑁 = 1664), where users had to choose their preferred news article profile from four different pairs. We found that a topical preference match was by far the strongest predictor for choosing a news article, while recency and demographic similarity had no impact.