Recommender system, Hybrid recommender system, Expert system, Collaborative-filtering, Content-based filtering, Movies, Rating

Currently, the Internet contains a large amount of information, which must then be filtered to deter- mine suitability for certain users. Recommender systems are a very suitable tool for this purpose. In this paper, we propose a monolithic hybrid recommender system called Predictory, which combines a recom- mender module composed of a collaborative filtering system (using the SVD algorithm), a content-based system, and a fuzzy expert system. The proposed system serves to recommend suitable movies. The sys- tem works with favorite and unpopular genres of the user, while the final list of recommended movies is determined using a fuzzy expert system, which evaluates the importance of the movies. The expert sys- tem works with several parameters –average movie rating, number of ratings, and the level of similarity between already rated movies. Therefore, our system achieves better results than traditional approaches, such as collaborative filtering systems, content-based systems, and weighted hybrid systems. The system verification based on standard metrics (precision, recall, F1-measure) achieves results over 80%. The main contribution is the creation of a complex hybrid system in the area of movie recommendation, which has been verified on a group of users using the MovieLens dataset and compared with other traditional recommender systems.