An intuitionistic fuzzy set based hybrid similarity model for recommender system

作者：

Highlights：

• An adjusted Google similarity is proposed in condition of sufficient co-rated items.

• A fuzzy set based KL similarity is proposed in condition of rare co-rated items.

• Proposed schemes are integrated in a certain range of co-rated items.

• Results reveal that our system has a favorable efficiency and accuracy.

摘要

•An adjusted Google similarity is proposed in condition of sufficient co-rated items.•A fuzzy set based KL similarity is proposed in condition of rare co-rated items.•Proposed schemes are integrated in a certain range of co-rated items.•Results reveal that our system has a favorable efficiency and accuracy.

论文关键词：Recommender system,Collaborative filtering,Normalized Google distance,Intuitionistic fuzzy set,Kullback–Leibler divergence

论文评审过程：Received 13 January 2019, Revised 28 March 2019, Accepted 5 June 2019, Available online 6 June 2019, Version of Record 14 June 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.06.008