# Movie Recommendation through Multiple Bias Analysis +
## 1 Introduction  
### Research Flow of Recommendation System  
Search engines have developed, thanks to the spread of the Internet, and the online transaction market has developed into a digital content market, thanks to the spread of smart devices. Consequently, more costs are now required for users to find information. Information retrieval (IR) [1] is a field to search for documents that are suitable for users' information needs, and recommender systems (RSs) are an application field that is derived from IR. The RS development has gone through a process, like that of IR, since RS developed and applied IR. An RS is an agent that recommends items that are suitable for users, and collaborative filtering (CF) is typically used. Regarding the analysis method, CF is divided into memory-based CF (User-based CF (UBCF) [2, 3]), Item-based CF (IBCF) [3, 4, 5, 6]), model-based CF (factorization model [7, 8, 9, 10]), content-based CF [11], and context-aware CF [12], and there is a hybrid model that synergizes the foregoing CF models to complement their advantages and disadvantages [13, 14]. The main issues of CF include cold start [15, 16], scalability [2, 6, 17], and accuracy. The cold start refers to a problem in which no recommendation is possible due to no record of decision-making for new users or new items--meaning that no items can be recommended to new users, and new items cannot be recommended to any users. Content-based CF is a representative model for solving cold-start problems, and it enables recommendations through metadata analysis, even without a record of decision-making. Scalability is a complex problem occurring in the real world and it entails difficulty in applying a small-scale research model to worksite operations. This problem is mitigated by simplifying or parallel processing the CF algorithm. In general, studies [2, 6] have been conducted for executing CF algorithms in a distributed/parallel processing environment,such as Hadoop [18] or Spark [19]. However, because the technical difficulty for parallelizing the algorithm is high, it is difficult to apply sophisticated or the latest CF. Apache Mahout [20] is an open source application programming interface (API) that is represented by CF in a distributed environment. Accuracy means how accurately the rated scores are predicted, and the matrix factorization (MF) model [8] has been evaluated as the most excellent so far. Furthermore, models aiming at extreme accuracy consider the viewpoints of ranking [10], zero-order [21], diversity [22; 23], etc., together. The core common feature of studies related to the foregoing is the enhanced accuracy obtained by deepening the existing analysis. Additionally, CF accuracy at a small adjustment level reflecting optimization according to domains was shown from NetFlix prize 09 [8] to neural collaborative filtering (NCF) [24]. Representative causes of the performance limit include the lack of data and the obsolescence of analysis algorithms. Deep learning is attracting attention for solving the existing performance limitations in the fields of speech recognition, image processing, and natural language processing, and recent study trends are also applying it to RS [24; 25; 26]. RS combined with deep learning is divided into text and rating analyses, and NCF is a representative study case. Rating analysis builds a model using the latent factors that are decomposed through factorization models (MF, Singular value decomposition (SVD) [9], SVD++ [8]) as inputs of neural networks (NNs), and NCF belongs to the foregoing. Text analysis means analyzing a user's review data, and the core is building it using natural language processing (NLP), like Word2 Vec or BERT (Bidirectional Encoder Representations from Transformers) [27]. Even recently, RSs using deep learning showed good performance results [25; 26], and RSs can be evaluated as a method for solving the two elements specified as the causes of limitations in the existing performance.