Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning
Table 5. Performance (H@3 in precent) comparison w.r.t. different hop numbers. %Improv. means the improvement ratio of UPGAN over the strongest baseline.
['Datasets', 'Hops', 'Baselines DistMult', 'Baselines ConvTransE', 'UPGAN', '%Improv.']
Performance Comparison w.r.t. Hop Number In our dataset, only the aligned KG entities correspond to interaction data from external application systems. We have constructed an interaction-augmented KG, and try to learn high-order relatedness between users and entities. The preference learned from such high-order relatedness has been verified to be effective in improving the KGC task. Hence, we would like to check how the distance of a KG entity to user nodes affects the performance. We consider three groups, namely aligned entities (1-hop), attributional entities corresponding to aligned entities (2-hop) and other entities (3-hop and more). It can be seen that our method has yielded a substantial improvement in all three groups. The finding indicates that our two-stage learning algorithm is able to perform effective information propagation and learning over the heterogeneous graph. Interestingly, the 1-hop entities do not always receive the most improvement. Indeed, we have found that the improvement is mainly related to the query difficulty instead of the hop number.