Persona-Aware Tips Generation
Table 3. ROUGE evaluation on the five datasets from different domains.
['[BOLD] Dataset', '[BOLD] Method', '[BOLD] ROUGE-1 R', '[BOLD] ROUGE-1 P', '[BOLD] ROUGE-1 F1', '[BOLD] ROUGE-2 R', '[BOLD] ROUGE-2 P', '[BOLD] ROUGE-2 F1', '[BOLD] ROUGE-L R', '[BOLD] ROUGE-L P', '[BOLD] ROUGE-L F1', '[BOLD] ROUGE-SU4 R', '[BOLD] ROUGE-SU4 P', '[BOLD] ROUGE-SU4 F1']
In order to capture more details, we report Recall, Precision, and F-measure (in percentage) of ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-SU4. Our model achieves the best performance in most of the metrics among all the five datasets. NRT does not consider persona information when generating tips. It only utilizes the learnt latent factors for users and items as the context information. Compared with NRT, our proposed framework PATG obtains better performance on all the metrics, which demonstrates that the consideration of persona information can indeed improve the tips generation performance. We also conduct statistical significance test comparing PATG and NRT and the results indicate that the improvements are significant with p<0.05.