Recommender systems are gaining a great importance with the emergence of E-commerce and business on the internet. These recommender systems help users in making decision by suggesting products and services that satisfy the users’ tastes and preferences. Collaborative filtering and content-based recommendation are two fundamental methods used to develop recommender systems. Although, both methods have their own advantages, they fail in some situations such as the ’cold start’ where new users or items are added in the system. In this paper, we propose an approach that combines collaborative filtering, content-based and demographic filtering approaches to develop a recommender system for predicting ratings in a dynamic way. We show through experiments that our approach achieves good accuracy and high coverage and outperforms the conventional filtering algorithms as well as the naive hybrid methods. Moreover, we show how our approach deals with the cold-start problem by incorporating demographic characteristics of users.
['Belkacem Chikhaoui', 'Mauricio Chiazzaro', 'Shengrui Wang']
An Improved Hybrid Recommender System by Combining Predictions