# Improving Graph-Based Movie Recommender System Using Cimenatic Experience +
## 4 Experiment
### Datasets
Accordingly, when features having orders are combined, it may be expected that the recommendation performance is increased. Meanwhile, Figure 7 shows the correlation between the emotion and rating, and shows that there is no clear correlation. By observing the dataset, we found that, when users felt the emotion of 'joy, love, surprise', they left high ratings, and when they felt the emotion of'sadness', they tended to leave low ratings. On the other hand, when they felt the emotion of 'fear', they often left relatively high ratings. _B. Compared methods_  
In the experiment, we chose multiple traditional and graph-based models for comparison. For the CF model, we investigated both item and user-based CF models. As matrix factorization has been widely adopted for commercial usage in recent days, we also included SVD-based models (SVD and SVD++). It should be noted that the proposed model is a graph-based approach for the recommendation, indicating that the current state-of-the-art graph-based model should be also included for the experiment. For this purpose, GCMC and IGMC models were selected, and those hyperparameters were determined by grid search to ensure their best performances. **Traditional models** KNNWithMeans, SVD, and SVDpp of the Surprise library were used to implement the traditional models. The KNNWithMeans model was a variant of CF and used as user and item-based CFs through the user_based option. The hyperparameters for the grid search were chosen to be best performing of the models. In the KNNWithMeans model, \(k\) were 10, 15, and 25, and _name_ were Pearson_baseline and Cosine. In the SVD and SVDpp models, learning rate were 0.005, 0.006, and 0.007. Figure 5: Long-tail graph in user dataset of Amazon. GCMC modelIn order to see the performance of individual models by the changes of the hyperparameters, the seed was fixed, and a grid search was conducted to perform hyperparameter tuning for each model. The final hyperparameters used for the experiment were as follows. Dimension was fixed at 75, 150, and 225. Drop outs were fixed at 0.3, 0.5, and 0.7. Learning rate was fixed at 0.005, 0.01, 0.02, and 0.03. C. Loss definition in IGMC  
Figure 6: Relation between sentiment and rating. Figure 7: Relation between emotion and rating. IGMC uses a unique type of loss, which consists of Mean Square Error (MSE) and Adjacent Rating Regularization (ARR) Loss. MSE loss is used for general regression based analysis that adjusts the weight of the models by learning the MSE difference between the prediction and actual values. However, graph neural network models such as GCMC and IGMC calculate the last output for each rating by Softmax and then transform it into a regression analysis form, so if only the MSE method is used, it can be learned without reflecting the similarity between each rating. For example, if the type of rating has scores from 1 to 5, score 1 and 2 are more similar than score 1 and 5. This distance between scores would not be reflected in the model. Equation (1) shows the MSE loss function that performs training process by reducing the difference between the predicted and actual ratings. Meanwhile, Equation (2) shows ARR loss function that trains adjacent ratings. The closer the rating scores are, the more similar the weights such as \(W_{r_{i}}\) and \(W_{r_{i+1}}\) are. \(\left\|\right\|_{F}\) indicates regulating matrices in a Frobenius norm. Equation (3) is the loss used when using only the rating in the IGMC method. However, because our model has three types of data, we need to define a new type of loss to reflect the characteristics of the IGMC model. As the sentiment is an ordinal data, Equation (4) is finally defined for our model applying the ARR loss functions on two data types such as ratings and sentiments. Here, because the emotion is a categorical type, it is not considered. Compared to the use of existing IGMC loss, the performance was slightly increased, but there was no significant effect.