Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning
Table 4. Performance (H@3 in precent) comparison w.r.t. different sparsity levels. %Improv. means the improvement ratio of UPGAN over the strongest baseline. We use “A−E” to denote the five groups with a decreasing sparsity level.
['Dataset', 'Models', 'A', 'B', 'C', 'D', 'E']
Performance Comparison w.r.t. Sparsity Levels In KG, different entities correspond to a varying number of triples. Various methods need sufficient training triples for learning good entity representations. Here, we examine how our method improves over the baseline methods, especially in the sparse case. For this purpose, we first divide the test queries into five groups w.r.t. the frequency of the answer entity. A smaller group ID indicates that the answer entity of that case occur fewer in training set. We can see that overall our approach is substantially better than over baseline methods in five sparsity levels. Especially, on movie and book datasets, it yields a larger improvement in sparse groups.