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A movie recommendation is a long-standing challenge. Figuring out the viewer’s interest in movies is still a problem since a huge number of movies are released in no time. In the meantime, people cannot enjoy all available new releases or unseen movies due to their limited time. They also still need to choose which movies to watch when they have spare time. This situation is not good for the movie business too. In order to satisfy people in choosing what movies to watch and to boost movie sales, a system that can recommend suitable movies is required, either unseen in the past or new releases. This paper focuses on the hybrid approach, a combination of content-based and collaborative filtering, using a graph-based model. This hybrid approach is proposed to overcome the drawbacks of combination in the content-based and collaborative filtering. The graph database, Neo4j is used to store the collaborative features, such as movies with its genres, and ratings. Since the movie’s closed caption is rarely considered to be used in a recommendation, the proposed method evaluates the impact of using this syntactic feature. From the early test, the combination of collaborative filtering and content-based using closed caption gives a slightly better result than without closed caption, especially in finding similar movies such as sequel or prequel.

Movie recommender systems using hybrid model based on graphs with co-rated, genre, and closed caption features