Beyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendation
Table 2. Diversity per article attribute. * indicates statistically significant difference with p<0.05.
['Attribute', 'manual', 'RecSys']
From the table, we see that the top 5 recommendations are more diverse than the highlighted articles in terms of ‘section’ and ‘word embeddings.’ Since there are many hundreds of unique tags, they typically exhibit a low overlap between articles, explaining the relatively high diversity in both sources. In terms of ‘authors,’ the manually curated highlighted articles show higher diversity than recommended articles. This can be explained by our finding that ‘authors’ and ‘tags’ are two of the most important user-article features in our models, which means recommendations will tend to be personalized more strongly towards ‘authors’ and ‘tags’ that are similar to our users’ reading history.