# Improving Graph-Based Movie Recommender System Using Cimenatic Experience +
## 3 Method  
In this section, we propose our model that considers both the rating and the CX information of sentiment and emotion together. Extracting the relations between the users and items can be achieved by passing the user reviews through the IGMC model. In the subsequent sections, we first describe the overview of the proposed model, which combines the predictions through multiple BERTs for the final prediction with IGMC. We then proceed to describe the BERTs and IGMC model that we developed for this study.  
A. Overview of the Proposed Model  
Figure 2 shows the overview of the proposed model as follows: First, movie and review data from Rotten Tomatoes and Amazon Product were preprocessed for excluding cold-start problems and divided into train/test data set. Second, in Rotten Tomatoes, the users' rating score was re-arranged by normalization as the Rotten Tomatoes rating scores were in various formats. The score interval of refined data was 0.5 from 1 to 5. Amazon data had already been normalized. Third, the user reviews were inputted into fine-tuned independent BERTs generating the prediction values as the output for the sentiment and emotion. The detailed operation process is presented in Algorithm 1. It describes the process of adding the relationship between user \(u\) and item \(v\) as a bipartite graph. Fourth, three graphs of the rating, sentiment, and emotion edges are entered into the graph model. Figure 3 shows the specific embedding process of the graph model. The final embedding was obtained by summing the three subgraph embedding vectors containing three types of information, producing richer information than the rating only. The corresponding operation process is presented in Algorithm 2. In the IGMC, graphs were embedded using RGCN. When three graphs were inputs to the RGCN, three vectors were outputs and were combined to generate an aggregated vector. Finally, we exploited Multi-Layer-Perceptron(MLP) to predict missing ratings for recommendation. The framework is based on the assumption, as reported in [19], the performance of the model could be further improved by exploiting CX data as side information.  
Figure 1: Link prediction process of IGMC.  
```
1:Input : The reviews \(r\)
2:Output : Two bipartite graphs \(G_{\text{s}}\), \(G_{\text{e}}\)
3:Generate two bipartite graphs \(G_{\text{s}}\), \(G_{\text{e}}\)
4:for\(r=r_{1},r_{2},\ldots\)do
5:\(s\gets [PERSON]_{\text{s}}(r)\)
6:\(e\gets [PERSON]_{\text{e}}(r)\)
7:
8:\(/*\) Adding a sentiment and emotion edge */
9:\(G_{\text{s}}(u,v)\gets s\)
10:\(G_{\text{e}}(u,v)\gets e\)
11:endfor
```  
**Algorithm 1** Generating graphs through BERT  
Figure 3: Aggregated subgraph embedding reflecting Cinematic Experience.  
Figure 2: Overview of the proposed framework.  
```
Input : Three bipartite graphs \(G_{r},G_{s},G_{e}\) Output : Aggregated embedding \(z_{v}\)
2: /* Sampling minibatch graphs */ Create graph collections G
4:for\((G_{r},G_{s},G_{e})=(G_{r}^{1},G_{s}^{1},G_{e}^{1}),(G_{r}^{2},G_{s}^{2},G_{e}^{2}),\ldots\)do \(G\leftarrow\text{Merge}(G_{r},G_{s},G_{e})\)
5:endfor
6: /* Generating embedding */ for\(G=G^{1},G^{2},\ldots\)do
7:\(G\rightarrow(G_{r},G_{s},G_{e})\) \((z_{r},z_{s},z_{e})\leftarrow\text{R-GCN}(G_{r},G_{s},G_{e})\)
8:\((z_{r}+z_{s}+z_{e})\to z_{v}\) endfor
```  
**Algorithm 2** Aggregating embedding