Improving Explainable Recommendations with Synthetic Reviews
Table 1. RMSE results
['[BOLD] Language Models', '[BOLD] Root mean square error']
To test these ideas, we first compare the generative models with the Test-pair baseline by applying related user and item reviews to the DeepCoNN recommender system to measure the performance of the generation models on the recommendation task. However, the proposed generative models perform better than the Test-pair baseline. The GCN models reach the best performance among our generative models. The effectiveness of the attention mechanism is observed, since the attention models present lower error than the context models in both character and word level. The best RMSE score of the generation models leads to a 5.3% improvement over the Test-pair baseline. These results indicate that synthetic reviews present more abundant information that fits the requirement of the recommender system than human written reviews. In Fig. The baseline and GCN models both make overestimates for reviews with lower ratings, and underestimate the reviews with highest rating (5). The average discrepancy is minimal for the most common rating value of 4. Although the GCN models have a similar distribution of predicted ratings to the baseline, the baseline clearly has more extreme outliers at both ends of the distribution. By filtering out the outliers across all rating values, the use of synthetic reviews improves on the state of the art rating prediction baseline model.