Neural Rating Regression with Abstractive Tips Generation…
Table 4. MAE and RMSE values for rating prediction.
['[EMPTY]', 'Books MAE', 'Books RMSE', 'Electronics MAE', 'Electronics RMSE', 'Movies MAE', 'Movies RMSE', 'Yelp-2016 MAE', 'Yelp-2016 RMSE']
It shows that our model consistently outperforms all comparative methods under both MAE and RMSE metrics on all datasets. From the comparison, we notice that the topic modeling based methods CTR and RMR are much better than LRMF, NMF, PMF, and SVD++. The reason is that CTR and RMR consider text information such as item specifications and user reviews to improve the representation quality of latent factors, while the traditional CF-based models (e.g. LRMF, NMF, PMF, and SVD++) only consider the rating matrix as the input. Statistical significance of differences between the performance of NRT and RMR, the best comparison method, is tested using a two-tailed paired t-test. The result shows that NRT is significantly better than RMR.