KRED: Knowledge-Aware Document Representation for News Recommendations
Table 3. Performance comparison of different models on new article classification and prediction tasks. Numbers in black bold indicate that the result is significantly (p-value<0.01) better than the best baseline method.
['[width=3.85cm, height=0.77cm]', 'Category Classification ACC', 'Category Classification F1-macro', 'Popularity Prediction ACC', 'Popularity Prediction F1-macro', 'Local News Detection AUC', 'Local News Detection ACC', 'Local News Detection F1']
The article category classification, article popularity prediction and local news detection tasks all belong to article-level classification problems. So we merge the performance comparison results into one table for concision. Entity information is not only important for personalized recommendation and item-to-item recommendation, but also is very important for the three article-level classification tasks. The reason is that data labels for the three article classification tasks are very limited. However, the data from user-item interactions are much more abundant. Jointly considering multiple tasks in the training process can share and transfer knowledge across domains so different tasks can benefit from each other, and the benefit of the low-resource domain is especially significant.