A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization
Table 4: Experimental results on the test set. [⋆] denotes the results reported in (Vu et al., 2017). Hits@1 (H@1) is reported in %. In information retrieval, Hits@1 is also referred to as P@1. The subscripts denote the relative improvement over our TransE results.
['[BOLD] Method', '[BOLD] MRR', '[BOLD] H@1']
Embedding models TranE, ConvKB and CapsE produce better ranking performances than traditional learning-to-rank search personalization models CI and SP. This indicates a prospective strategy of expanding the triple embedding models to improve the ranking quality of the search personalization systems. In particular, our MRR and Hits@1 scores are higher than those of TransE (with relative improvements of 14.5% and 22% over TransE, respectively). Specifically, our CapsE achieves the highest performances in both MRR and Hits@1 (our improvements over all five baselines are statistically significant with p<0.05 using the paired t-test).