An Ontological Hybrid Recommender System for Dealing with Cold Start Problem

Recommender systems that predict user ratings for a set of items are known as subset of information filtration systems. They help users find their favorite items from thousands of available items. One of the most important and challenging problems that recommendation systems suffer from is the problem of dispersion. This means that due to the scatter of data in the system, they are not able to find popular items with the desired reliability and accuracy. This is especially true when there are a large number of items and users in the system and the filled ratings are low. Another challenging problem that these systems suffer from is their scalability. One of the major problems with these systems is the cold start. This problem occurs due to the small number of items rated by the user, i.e. the scatter of users. This problem is divided into two categories: new user and new item. The main focus of this article is on the problem of the new user type. This problem occurs when a new user has just logged in and has not rated any item yet, or when the user has already logged in but has been less active in rating. The goal is to address these three challenges. In this study, an ontology-based hybrid recommender system is introduced in which ontology is used in the content-based filtering section, while the ontology structure is improved by the collaborative filtering section. In this paper, a new hybrid approach based on combining demographic similarity and cosine similarity between users is presented in order to solve the cold start problem of the new user type. Also, a new approach based on combining ontological similarity and cosine similarity between items is proposed to solve the cold start problem of the new item type. The main idea of the proposed method is to extend users’/items’ profiles based on different mechanisms to create higher-performance profiles for users/items. The proposed method is evaluated in a real data set, and experiments show that the proposed method performs better than the advanced recommender system methods, especially in the case of cold start.