Acknowledgements
Table V.10: Kendall-Tau correlation of different models across domains.
['[BOLD] Domain', '[BOLD] Kendall-Tau-B ( [ITALIC] τb)  [BOLD] Baseline', '[BOLD] Kendall-Tau-B ( [ITALIC] τb)  [ITALIC] MYelp [BOLD]  (C-SVM)', '[BOLD] Kendall-Tau-M ( [ITALIC] τm)  [BOLD] Baseline', '[BOLD] Kendall-Tau-M ( [ITALIC] τm)  [ITALIC] MYelp [BOLD]  (C-SVM)', '[BOLD] Kendall-Tau ( [ITALIC] τb= [ITALIC] τm)  [ITALIC] MAmazon [BOLD]  (SVM-Rank)']
Ch4S3SS4SSSx3 Ch4S4SS1SSSx2 -Ch5S3SS1SSSx4 Ch5S3SS1SSSx7 -Ch5S3SS2SSSx3 Ch5S3SS2SSSx6 Ch5S3SS3SSSx4 Ch5S3SS3SSSx5 Ch5S4SS2SSSx2 -Ch5S4SS3SSSx3 Ch5T13 The baseline for the item ranking is based on the aggregated rating of all reviews on an item. The first model MYelp (C-SVM) trained on Yelp filters out the non-credible reviews, before aggregating review ratings on an item. The second model MAmazon (SVM-Rank) is trained on Amazon using SVM-Rank with the reference ranking as training labels. with respect to the reference ranking. τb and τm for SVM-Rank are the same since there are no ties. Our first model performs substantially better than the baseline, which, in turn, is outperformed by our second model. The second model MAmazon — trained on Amazon using SVM-Rank — outperforms the former exploiting the power of the entire feature space and domain-specific proxy labels unavailable to the former.