# Improving Graph-Based Movie Recommender System Using Cimenatic Experience +
## 1 Introduction  
In recent years, users have been exposed to a large volume of multimedia contents due to the emergence of Over-the-Top (OTT) platforms such as Netflix and Disney Plus, highlighting the importance of developing content recommender systems that identify users' tastes from their daily usage logs and recommend serendipitous contents that users may potentially like, so that users are can be stayed and tuned longer on the OTT platform services. The most well-known recommendation strategy of commercial OTT services is to use users' rating data, which is then fed for collaborative filtering [1]. However, the taste of movies can be represented in various types of experiences other than the rating scores, and those experiences are often defined as Cinematic Experience (CX) [2]. Generally, it is a concept that refers to the interplay between inner mental reality and outer material reality when watching a movie. Specifically, it refers to the environment that immerses a movie or the psychological context and aftertaste when watching a movie, meaning that CX would require more numerical and categorical forms such as sentiment and emotion of the users, to be used for contents recommendation. One of the representative data that can reflect the user's CX are sentiment and emotion, and they are collected and used individually for personalized recommendations by OTT platforms. For instance, Youtube collects not only the sentiment feedback viafive-point scales from negative to positive but also the emotion feedback by asking users about their feelings after watching the recommended item. Similarly, some of previous studies have shown that sentiment or emotion information improves recommendation performance [1; 3; 4]. However, to the best of our understanding, no studies have used both of these two CX elements simultaneously for a recommender system. In fact, sentiment and emotion are clearly distinguished in two measurement types: ordinal type and categorical type. First, sentiment typically divides the user's positive and negative feelings into five levels, such as negative, somewhat negative, neutral, somewhat positive, and positive, and it is characterized by having an order. Second, emotion commonly divides the user's psychological feelings into six levels, such as anger, fear, joy, love, sadness, and surprise; and it is characterized by having categories without orders. The dimension difference between the sentiment and emotion has challenged combining these data together for further recommendation, addressing the need of exploiting new approaches to embed multiple CX experiences into one dimension to enhance the user retention on the OTT platforms. Recently, the recommendation method, which constructs the interaction between user and movie in a graph and reflects metadata and relations through the graph-natural-network(GNN), has become popular [5; 6; 7; 8]. In this paper, we propose a new graph-based movie recommender system with BERT, which utilizes all of the rating, sentiment, and emotion information by considering the differences of the information in different dimensions. Inductive Graph-based Matrix Completion (IGMC) [6] is a link prediction method that transforms the rating matrix of user-item into a bipartite graph and predicts missing rating using subgraph embeddings. It is worth noting that the proposed model obtains user's psychological context using fine-tuned BERT for sentiment and emotion, and predicts final ratings using IGMC by combining BERT results and rating information into a subgraph.