  In the world of big data, many people find it difficult to access the
information they need quickly and accurately. In order to overcome this,
research on the system that recommends information accurately to users is
continuously conducted. Collaborative Filtering is one of the famous algorithms
among the most used in the industry. However, collaborative filtering is
difficult to use in online systems because user recommendation is highly
volatile in recommendation quality and requires computation using large
matrices. To overcome this problem, this paper proposes a method similar to
database queries and a clustering method (Contents Navigator) originating from
a complex network.
