# Improving Graph-Based Movie Recommender System Using Cimenatic Experience +
## 3 Method
### Predictions for sentiment and emotion by BERT  
We used the BERT model to extract sentiment and emotion information from the review text. BERT is one of the state-of-the-art transformers that has been proven to be outperforming for various tasks such as sentiment analysis, preference analysis, and conversation purpose classification through fine-tuning process [18]. To fine-tune the BERT models, we used training datasets for sentiment analysis ([[https://www.kaggle.com/c/sentiment-analysis-on-movie-reviews/data](https://www.kaggle.com/c/sentiment-analysis-on-movie-reviews/data)]([https://www.kaggle.com/c/sentiment-analysis-on-movie-reviews/data](https://www.kaggle.com/c/sentiment-analysis-on-movie-reviews/data)), accessed on 12 December 2021) and emotion analysis ([[https://www.kaggle.com/praveengovi/emotions-dataset-for-nlp](https://www.kaggle.com/praveengovi/emotions-dataset-for-nlp)]([https://www.kaggle.com/praveengovi/emotions-dataset-for-nlp](https://www.kaggle.com/praveengovi/emotions-dataset-for-nlp)), accessed on 12 December 2021), available in Kaggle. The accuracy of each BERT model was 70% for sentiment analysis and 90.4% for emotion analysis for the testing set. The data created through the fine-tuned BERT was added as relations on the user-item graph. In the Experiment section, the correlation between the output result of the BERT model and the rating will be described.  
_C. IGMC model_  
As shown in Figure 1, the existing IGMC model [6] transforms a rating matrix of the user-item into a bipartite graph and uses the MLP to predict the unobserved interaction between the user and the item using subgraph embedding. IGMC learns the patterns of subgraphs, not the entire graph, and the model is inductive, so it can predict the rating even if some unlearned data are entered. The components of the model are largely composed of four steps.  
* Enclosing subgraph extraction In the entire Bipartite graph, it expands to neighbors around specific users and items to create a subgraph. We add as many new nodes as \(h\)-hope connected to the user and item sets to the subgraph node set. Thereafter, edges associated with nodes that do not belong to this set are deleted from the entire graph. Finally, the edge is deleted between the target users and items for the rating prediction.
* Node labeling: In order for the model to be inductive, it is necessary to shake off the global context for generalization. So, a new label is attached to read the subgraph's pattern only.
* Passing graph neural networks: When the features of the subgraph are extracted using GNN, each node has a unique embedding of the assigned label. In the proposed model, a relational-GNN(R-GNN) that extends GNN to grasp other features according to the type of edge, is exploited. The GNN passes through several layers of GNN layers, and the output of all layers is concatenated, defined as the final representation of the node.
* Rating prediction:Among the feature vectors of the node identified through R-GNN, the feature vectors of users and items for which we want to predict the score are concatenated and defined as the final expression of the subgraph. Finally, the rating is returned by passing it through the MLP layer.