Exploring Latent Semantic Factors to Find Useful Product Reviews
Table 2: Prediction Task: Performance comparison of our model versus baselines. Our improvements over the baselines are statistically significant at p-value <2.2e−16 using paired sample t-test.
['[BOLD] Models', '[BOLD] Mean Squared Error (MSE)  [BOLD] Movies', '[BOLD] Mean Squared Error (MSE)  [BOLD] Music', '[BOLD] Mean Squared Error (MSE)  [BOLD] Books', '[BOLD] Mean Squared Error (MSE)  [BOLD] Food', '[BOLD] Mean Squared Error (MSE)  [BOLD] Electr.', '[BOLD] Squared Correlation Coefficient ( [ITALIC] R2)  [BOLD] Movies', '[BOLD] Squared Correlation Coefficient ( [ITALIC] R2)  [BOLD] Music', '[BOLD] Squared Correlation Coefficient ( [ITALIC] R2)  [BOLD] Books', '[BOLD] Squared Correlation Coefficient ( [ITALIC] R2)  [BOLD] Food', '[BOLD] Squared Correlation Coefficient ( [ITALIC] R2)  [BOLD] Electr.']
For all of the above baselines, we use all the features from their works that are supported by our dataset — for instance, we could not use the social network, and explicit product meta-data absent in our dataset — for a fair comparison. Our model consistently outperforms all baselines in reducing the MSE. Interpretable explanation by salient words used by experts for helpful reviews: