KRED: Knowledge-Aware Document Representation for News Recommendations
Table 4. Ablation studies. Removing any of the layers in KRED leads to a significant (p-value<0.01) performance drop.
['Model KRED', 'AUC  [BOLD] 0.6894', '[EMPTY]', 'NDCG@10  [BOLD] 0.2673', '[EMPTY]']
Next we investigate how each layer contributes to KRED. Since there are three key layers in KRED, we remove one layer each time and test if the performance is influenced. Because the conclusions on different tasks are similar, we only report the results of KREDBERT on the personalized recommendation task to save space. We observe that removing either one layer will cause a significant performance drop, which indicates that all layers are necessary.