User Group-based Method for Cold-Start Recommendation

doi:10.23940/ijpe.18.08.p8.17191725

Abstract

Abstract:

Recommendation algorithms seek to predict user ratings or preferences. Due to limited information, it is difficult to make these predictions for new users. Therefore, a dynamic cold-start recommendation algorithm would be highly helpful in such quick-changing social networks. In this paper, a novel user group-based collaborative method, called UCFRA (User group-based Cold-start Friend Recommendation Algorithm) is proposed. UCFRA integrates a graphical model and statistical population characteristics into a user group model and then combines this extended user group model with cold-start information to generate a new recommendation algorithm. Moreover, a content popularity model based on user groups and a user rating matrix is designed. In order to improve recommend precision, a user group Top-N recommendation model based on k-nearest neighbors is provided. A series of experiments involving collection of a huge data set was developed to evaluate the effectiveness of UCFRA. The experimental results showed that UCFRA is a valid algorithm.

Submitted on April 29, 2018; Revised on June 15, 2018; Accepted on July 14, 2018
References: 21

Jing He, Shuo Yuan, Yi Xiang, and Wei Zhou. User Group-based Method for Cold-Start Recommendation [J]. Int J Performability Eng, 2018, 14(8): 1719-1725.

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