Hybrid recommender system with core users selection

Recommender system plays an increasingly important role in identifying the individual's preference and accordingly makes a personalized recommendation. Matrix factorization is currently the most popular model-based collaborative filtering (CF) method that achieves high recommendation accuracy. However, similarity computation hinders the development of CF-based recommendation systems. Preference obtained only depends on the explicit rating without considering the implicit content feature, which is the root cause of preference bias. In this paper, the content feature of items described by fuzzy sets is integrated into the similarity computation, which helps to improve the accuracy of user preference modeling. The importance of a user is then defined according to preferences, which serves as a baseline standards of the core users selection. Furthermore, core users based matrix factorization model (CU-FHR) is established, then genetic algorithm is used to predict the missing rating on items. Finally, MovieLens is used to test the performance of our proposed method. Experiments show CU-FHR achieves better accuracy in prediction compared with the other recommendation methods.

Automated summary

Recommender system is crucial for personalized recommendation. The paper introduces a model-based collaborative filtering method and its challenges, proposes a method of integrating content features described by fuzzy sets into similarity computation, and establishes a matrix factorization model based on core users. Experiments show that the proposed method achieves better prediction performance.