# Improving Graph-Based Movie Recommender System Using Cimenatic Experience +
## 2 Related Work  
Recommender systems select the most appropriate items to a specific user based on user profiles, item profiles, and user-item interactions. Recommender systems have different types of filtering such as content-based, collaborative, demographic-based, and hybrid [11]. Recently, many studies have adopted neural network architectures to mine complex, and nonlinear patterns of user-item interactions [12]. There largely exists two types of approaches to contents recommendation: rating-only and multi-relational based recommendation. First, recommendation models in [13; 14] predicted a future rating of a user by using the user reviews as input data. In such methods, sophisticated recommendation became difficult when there was a substantial number of neutral ratings. As the number of user reviews was limited to track the individual user interests, various sources such as user tags [15] and user behavior logs [16] were further exploited for contents recommendations. Second, in [17], sentimental analysis was conducted in a statistical-based manner based on an sentimental dictionary, text mining, natural language processing (NLP) [18], but only two levels (positive, negative) or three levels (positive, neutral, negative) were utilized. Indeed, it is essential to further subdivide the user's psychological information for better recommendation. However there yet exists the difficulty to extract the user's psychology in detail by dividing sentiment into a smaller level. Although the sentiment analysis would be successfully conducted at a smaller level, it is yet difficult to use data altogether because the dimensions of sentiment and emotion are different. For example, in [PERSON] et al's study [19], positive and negative signals were scaled to \(-0.5\) to \(0.5\) to reflect the results of sentiment analysis in the rating, combined with rating, and then used in the recommendation system. Similarly, if emotion information is to be reflected in the recommendation, scaling becomes problematic as it is not ordinal but categorical, suggesting that a new method should be considered because a native linear combination faces the lack of the effectiveness in recommendation due to the dimension difference of multiple relations. Previous studies have shown that sentiment or emotion information improves recommendation performance [1; 3; 4; 20; 21]. To the best of our knowledge, however, no studies have used both of these two in combination for recommender systems. Existing recommender systems range from traditional statistical-based methods to deep learning methods. Traditional methods can be further divided into memory-based or model-based approaches. In memory-based approach, collaborative filtering (CF) [22; 23] is a widely used algorithm and can be divided into user-based or item-based CFs according to criteria for similarity comparison. User-based CF is a method of identifying a user's neighbors whose preferences are similar using their historical information. On the other hand, item-based CF is a method of identifying neighbors of items. In the model-based approach, matrix factorization (MF) [24; 25] predicts the missing rating of the sparse matrix by dividing the rating matrix into the latent user matrix and the latent item matrix. As one of the representative linear algebra techniques for matrix decomposition, singular value decomposition (SVD) [26] predicts the missing rating by dividing the rating matrix into \(m^{*}m\) orthogonal matrix, \(m^{*}n\) rectangular diagonal matrix (singular values), and \(n^{*}n\) orthogonal matrix. In this method, \(m\) is the number of users, and \(n\) is the number of movies. Furthermore, SVD++ is employed as an optimized SVD algorithm to enhance the accuracy of prediction by generating implicit feedback. Recently, recommendation methods based on graph neural networks (GNNs) have gained attention. GNN-based methods learn user-item relations from graph data and predict new relations.