Design of nonlinear data-based wellness content recommendation algorithm

Abstract

As IT technology has advanced and people’s interest in wellness has increased, recommendation algorithms are being developed to allow people to use wellness content easily. However, existing recommendation algorithms use data entered by users and content-based filtering to recommend content, making it difficult to recommend areas of interest which change in real time. Therefore, in this paper we propose an algorithm which creates user information based on nonlinear social network data and makes recommendations in real time in order to reflect the user’s recent interests. The test result verified that the proposed algorithm improved accuracy by 31% compared to that of the existing content-based recommendation algorithm. © Springer Nature Singapore Pte Ltd. 2018.