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

Performance of Recommender Systems: Based on Content Navigator and Collaborative Filtering