Credible Review Detection with Limited Information using Consistency Features
Table 5: Variation of Kendall-Tau-M (τm) correlation with #reviews with MAmazon (SVM-Rank).
['[BOLD] Domain', '[ITALIC] τm [BOLD]  with #reviews per-item ≤5', '[ITALIC] τm [BOLD]  with #reviews per-item ≤10', '[ITALIC] τm [BOLD]  with #reviews per-item ≤20', '[ITALIC] τm [BOLD]  with #reviews per-item ≤30', '[ITALIC] τm [BOLD]  with #reviews per-item ≤40', '[ITALIC] τm [BOLD]  with #reviews per-item ≤50', '[ITALIC] τm [BOLD]  with #reviews per-item Overall']
In order to find the effectiveness of our approach in dealing with “long-tail” items, we perform an additional experiment with our best performing model i.e., MAmazon (SVM-Rank). We observe that our model performs substantially well even with items having as few as five reviews, with the performance progressively getting better with more reviews per-item. “Long-Tail” Items: Nevertheless, we observe it to give a substantial Kendall-Tau correlation (τm) with the reference ranking, with as few as five reviews per-item, demonstrating the effectiveness of our model in dealing with “long-tail” items.