Recommended systems are one of the most important techniques used to introduce information about user needs, including related services, by analyzing user actions [1, 2]. For the recommender system, a collaborative filtering approach is used to introduce information that will meet the needs of the user. The collaborative filtering is based on similarly tasteless users, the same choice, and the idea that users who buy in the past will buy in the future [3]. Data production factors for the collaborative filtering process are user interest or user behavior in the form of the feature vector. This vector is paired with all other user carriers, and the most similar users are selected to be made in the vicinity of the user. From there, the guide contains information about things previously liked by users in their neighborhood [4]. However, collaborative filtering often suffers from vulnerabilities [5] that affect the quality of their neighborhood. Use like Cold-start, Sparsity, and Rating credibility.

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