Café Recommendation Using the Content-Based Filtering Method
Abstract
The coffee industry has experienced rapid growth over the last decade. In this research, the content-based filtering approach is employed to suggest cafes by analyzing the similarity of different features or attributes. The degree of similarity is influenced by the similarity of item profiles between cafes. CW Coffee & Eatery had the highest similarity value of 0.4802 because it found 16 item profiles that were similar to Cosan Seturan. In contrast, Kelanaloka has a very low similarity value of 0.1844, because only 7 similar item profiles were identified when compared. This research shows that content-based filtering methods can be effectively applied to cafe recommendation systems.
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References
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