A Novel Hybrid Similarity Calculation Model

作者：Fan, Xiaoping; Chen, Zhijie; Zhu, Liangkun; Liao, Zhifang^*; Fu, Bencai

来源：Scientific Programming, 2017, 2017: 4379141.

DOI：10.1155/2017/4379141

• USER SIMILARITY
• ACCURACY

This paper addresses the problems of similarity calculation in the traditional recommendation algorithms of nearest neighbor collaborative filtering, especially the failure in describing dynamic user preference. Proceeding from the perspective of solving the problem of user interest drift, a new hybrid similarity calculationmodel is proposed in this paper. Thismodel consists of two parts, on the one hand the model uses the function fitting to describe users' rating behaviors and their rating preferences, and on the other hand it employs the Random Forest algorithm to take user attribute features into account. Furthermore, the paper combines the two parts to build a new hybrid similarity calculationmodel for user recommendation. Experimental results show that, for data sets of different size, the model's prediction precision is higher than the traditional recommendation algorithms.

• 出版日期2017
• 单位湖南财政经济学院