A Probabilistic Framework for Learning Domain SpecificHierarchical Word Embeddings
Table 3: Comparison of embedding approaches for predicting Star Rating of reviews. Lower is better.
['Method', 'MAE', 'RMSE']
We split the dataset in 80-10-10 proportions for train-development-test and report mean absolute error (MAE) and root mean squared error (RMSE) over test set after averaging five runs with random partitions. We can see that all methods that include text perform better than NeuMF which only uses the user id and item id. This shows the benefit of utilizing the textual information. We also observe that both of the Word2Vec algorithms outperform GloVe embeddings, demonstrating the domain dependence of word meanings that are not captured in GloVe embeddings pre-trained on Wikipedia and Gigaword. Finally, the proposed Hierarchical Embeddings provide the best text representation and achieve the lowest error rate. By learning domain specific word embeddings, the hierarchical model can represent appropriate usage of a word, leading to better downstream predictions.