DevCoach 162: Machine Learning | Mengoptimalkan Pengalaman Pengguna dengan Sistem Rekomendasi

Transcript

1. Mengoptimalkan Pengalaman Pengguna dengan Sistem Rekomendasi Machine Learning Mochamad Rafy Ardhanie Curriculum Developer
2. Key Point Machine Learning • Pengenalan sistem rekomendasi • Ragam sistem rekomendasi • Content based filtering • Collaborative filtering • Hybrid Recommender • Be a friend with math • Code for lyfe
3. Machine Learning
4. Machine Learning
5. Machine Learning
6. Machine Learning
7. Pengenalan Sistem Rekomendasi Machine Learning
8. Machine Learning Sistem Rekomendasi A system that predicts ratings or preferences a user might give to a product. Often these are sorted and presented as “top-N'' recommendations. Also known as recommender engines, recommendation systems, and recommendation platforms
9. Machine Learning Mengapa Sistem Rekomendasi itu Penting?
10. Machine Learning Perusahaan Pengguna
11. Machine Learning
12. Machine Learning https://atrium.ai/resources/what-are-recommendation-systems-and-how-are-they-transforming-our-markets/
13. Machine Learning
14. Machine Learning Meningkatkan Penjualan Mengapa Sistem Rekomendasi itu Penting? Penjualan Beragam Kepuasan Pelanggan Personalisasi
15. Ragam Sistem Rekomendasi Machine Learning
16. Machine Learning
17. Machine Learning Non-Personalized Recommender System
18. Machine Learning Best-Seller People who X also Y Most Popular Best-Liked Trending Hot Non-Personalized Recommender System
19. Machine Learning Personalized Recommender System Personalized Collaborative Filtering Content-Based Filtering User Based Item Based Hybrid Method Matrix Factorization Deep Learning Memory Based Model Based
20. Content-Based Filtering Machine Learning
21. Machine Learning Content-Based Filtering
22. Machine Learning Content-Based Filtering
23. Machine Learning Content-Based Filtering A.B = (1*1)+(1*1)+(1*1)+(0*0)+(0*0) ||A|| = √12+12+12 =1 ||B|| = √12+12+12 =1 Cosine Similarity = 1
24. Machine Learning Content-Based Filtering Genre: Action = [1, 0], Adventure = [0, 1] Platform: PC = [1, 0], Console = [0, 1] Rating: nilai rating itu sendiri Game A: [1, 0, 1, 0, 4.5] Game B: [1, 0, 0, 1, 4.0] Game C: [0, 1, 1, 0, 3.5]
25. Machine Learning Content-Based Filtering
26. Machine Learning Content-Based Filtering
27. Collaborative Filtering Machine Learning
28. Machine Learning Collaborative Filtering
29. User-Based
30. Item-Based
31. Model-Based Cluster Based Matrix Factorization Deep Learning
32. Hybrid Recommender System Machine Learning
34. Machine Learning
35. Hybrid Recommender System Mixed Weight Switching
36. Be a Friend with Math Machine Learning
37. Similarity Measure Bagaimana Anda mendefinisikan kesamaan (kemiripan) ini? Kesamaan paling umum yang bisa diukur dalam sistem rekomendasi antara lain, preferensi dan selera. Selain itu kesamaan juga dapat ditemukan melalui data dan informasi lain seperti demografi pengguna dan status sosial.
38. Jaccard Distance Teknik ini menghitung seberapa dekat jarak antara dua set sampel. Dalam hal ini, set sampel bisa berupa sekumpulan restoran yang disukai pengunjung. Koefisien ini mengukur kesamaan dengan menghitung irisan antara sampel dibagi dengan gabungan sampel.
39. Cosine Similarity Cosine similarity mengukur kesamaan antara dua vektor dan menentukan apakah kedua vektor tersebut menunjuk ke arah yang sama. Ia menghitung sudut cosinus antara dua vektor. Semakin kecil sudut cosinus, semakin besar nilai cosine similarity.
40. Euclidean Distance Metode Euclidean distance dihitung berdasarkan jarak antara dua titik (item). Item serupa letaknya akan berdekatan satu sama lain jika diplot dalam ruang berdimensi-n.
41. Code for Lyfe
42. Thank You Rafyardhani Rafy@dicoding.com Rafyardhani RafyArdhanie Machine Learning
43. Feedback! Hadiah: • 1 Token Langganan Academy 30 Hari) *untuk pengisi feedback terpilih! dicoding.id/devcoachfeedback