Convolutional Quantum-Like Language Model with Mutual-Attention for Product Rating Prediction
Table 2. Performance comparison on eight datasets for all methods (MAE).
['[EMPTY]', 'Musical Instruments', 'Automo-tive', 'Amazon Instant Video', 'Office Products', 'Patio Lawn and Garden', 'Grocery and Gourmet Food', 'Digital Music', 'Yelp']
From these results, several observations can be made. As argued previously, despite the fact that textual information is useful in recommendation, there is no guarantee that introducing textual information will improve model performance. The performance can vary depending on how textual information is incorporated into each model effectively. In our model, we propose a quantum-like density matrix layer on top of convolutional layers to learn covariances between latent features, and a new mutual-attention layer for user and item density matrix to communicate information from counterparts.