<![CDATA[Skincare refers to skin care products. These products have different purposes depending on the user's skin type. Over time, public awareness and interest in skincare will increase, leading to a rapid growth of skincare products. Therefore, an Item-Based Collaborative Filtering (CF) method is used to develop a skincare recommendation system. This method will provide personalised recommendations by leveraging the behaviour data of other users with similar preferences and characteristics. This study uses user ratings and preferences for skincare products as data. This data is then used to build a CF model, which will be analysed to calculate user similarity patterns using the cosine similarity matrix. The application of the CF method demonstrates its effectiveness in matching user preferences, resulting in the most relevant product recommendations. This system not only increases the accuracy of recommendations but also helps users find products that meet their skin care needs, as the error rate in the system is 0.245.]]>
