Tags and Item Features as a Bridge for Cross-Domain Recommender Systems

In this paper, we propose a model by utilizing item features and user-generated tags through matrix factorization in CDRSs framework. Firstly, we extract item features in terms of genres and user preferences in terms of user-generated tags. Thereafter, to establish the bridge for transferring knowledge, matrix factorization has been used. Finally, experimental results demonstrate that our proposed model outperforms the other single domain as well as cross domain approaches in CDRSs framework.