A Semantic-Based Recommendation Approach for Cold-Start Problem

Recommender systems (RS) can predict a list of items which are appropriated to users by using collaborative or content-based filtering methods. The former is more popular than the latter approach, however, it suffers from cold-start problem which can be known as new-user or new-item problems. Since the user/item firstly appears in the system, the RS has no data (feedback) to learn, thus, it cannot provide any recommendation. In this work, we propose using a semantic-based approach to tackle the cold-start problem in recommender systems. With this approach, we create a semantic model to retrieve past similarity data given a new user. Experimental results show that the proposed approach works well for the cold-start problem.