# Improving Graph-Based Movie Recommender System Using Cimenatic Experience +
## 4 Experiment  
### Datasets  
We used two movie datasets: Rotten Tomatoes and Amazon Review. Because each dataset was rated in a different type of rating system, two different datasets were used to see if the proposed model could reflect all types of rating systems, from general to particular types of rating systems. For example, the Amazon dataset is used by general users, and the ratings range from 1 to 5. However, Rotten Tomatoes Dataset is a dataset crawled by a specific researcher and provided to Kaggle, and has an unstructured rating such as categorical type (A, B, C, D), continuous type (0.75, 0.573), and fractional type (1/5, 35/70). In the subsequent sections, the preprocessing process of each dataset is presented. **Rotten Tomatoes movie dataset** We used a Kaggle dataset ([[https://www.kaggle.com/stefanoleene992/roten-tomatoes-movies-and-critic-reviews-dataset](https://www.kaggle.com/stefanoleene992/roten-tomatoes-movies-and-critic-reviews-dataset)]([https://www.kaggle.com/stefanoleene992/roten-tomatoes-movies-and-critic-reviews-dataset](https://www.kaggle.com/stefanoleene992/roten-tomatoes-movies-and-critic-reviews-dataset)), last accessed: 15 October 2021), which has been constructed by crawling movies' meta-data and review data from the Rotten Tomatoes website. The dataset was divided into movie data and rating data, where the number of the user ratings and reviews were 758,709 in total, accounting for 67% of the whole dataset. There were three considerations when building the dataset. First, users with more than 20 ratings were selected to minimize the effect of the long-tail as shown in Figure 4. Second, when building the training and testing sets, it was divided based on the timestamps to find out if the model successfully predicts the future ratings based on the historical data. Third, user_ids and movie_ids existing in the training set remained only in the testing set for constant observation. After all, 1112 user_ids and 8521 movie_ids were used. For node embedding of GCMC, 'title, year, and genres' were used in the movie data, and 'top_critic and publisher_name' were used in the user data. Because movie_id and user_id did not exist in the original dataset, movie_ids were attached in index order in the movie_data, and user_ids were attached in groups of 'critical_name, top_critical, and publisher_name' in the user_data. **Amazon Review dataset** We used Amazon product data, a popular benchmark dataset [30; 31; 32; 33] containing product reviews and metadata of Amazon from 1996 to 2018. It also includes various categories such as Books, Electronics, Movies and TV, and Beauty. Among the categories, we used Movies and TV data, given the purpose of our experiment. First, we used the smaller version of the dataset (about 1,697,533 reviews) and chose users with  
Figure 4: Long-tail graph in user dataset of Rotten Tomatoes. more than 50 reviews to exclude the cold-start problem. Figure 5 shows the form of a long-tail graph as in Rotten Tomatoes. Second, train and test data were divided at a ratio of 8:2 according to the timestamp. Finally, 3264 user_ids and 14,124 movie_ids were used. Because the Amazon product data does not provide meta information of movies, the GCMC model does not use node features in the Amazon product data. Feature generating using fine-tuned BERTTo learn BERT, data from 'Sentimental Analysis on Movie Reviews' and 'Emotions Dataset for NLP' in Kaggle were used. After fine-tuning two independent BERTs using the training data sets, the review data were entered into the BERTs and the predicted results were used as the labels for user_id and movie_id. It was observed that the sentiment data were highly correlated with its rating. On the other hand, in the emotion data, no clear correlation was seen. Figure 6 shows the correlation between the sentiment and rating. The higher the positive sentiment, the higher the rating is, indicating that there is a positive correlation between the two variables.