Affiliations: [a] Department of Computer Engineering, Science and Research branch, Islamic Azad University, Tehran, IR | [b] Department of Computer Engineering, Iran University of Science and Technology, Tehran, IR | [c] Department of Computer Engineering, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, IR | [d] Young Researchers and Elite Club, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, IR | [e] Department of Electrical Engineering, Persian Gulf University, Bushehr, IR | [f]
The Graduate School of Biomedical Engineering, UNSW Australia, Sydney, AU | [g]
School of Computer Science and Engineering, UNSW Australia, Sydney, AU
Abstract: Recommender Systems (RS) are expected to suggest the accurate goods to the consumers. Cold start is the most important challenge for RSs. Recent hybrid RSs combine ConF and ColF. We introduce an ontological hybrid RS where the ontology has been employed in its ConF part while improving the ontology structure by its ColF part. In this paper, a new hybrid approach is proposed based on the combination of demographic similarity and cosine similarity between users in order to solve the cold start problem of new user type. Also, a new approach is proposed based on the combination of ontological similarity and cosine similarity between items in order to solve the cold start problem of new item type. The main idea of the proposed method is to expand user/item profiles based on different strategies to build higher-performing profiles for users/items. The proposed method has been evaluated on a real dataset and the experimentations indicate the proposed method has the better performance comparing with the state of the art RS methods, especially in the case of the cold start.
