Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari...
Transcript of Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari...
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Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Recommendation System
Recommendation System
![Page 2: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/2.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Introduction
Recommenders vs Search Engines
Kategori recommendation system
Input recommendation system
Collaborative Filtering
Content-based
Evaluasi recommendation system
Masalah dalam recommendation system
Recommendation System
![Page 3: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/3.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Introduction
Recommenders vs Search Engines
Kategori recommendation system
Input recommendation system
Collaborative Filtering
Content-based
Evaluasi recommendation system
Masalah dalam recommendation system
Recommendation System
![Page 4: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/4.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Introduction
Apa recommendation system (sistem rekomendasi)?Contoh?
Recommendation System
![Page 5: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/5.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Introduction
Apa recommendation system (sistem rekomendasi)?Contoh?
Recommendation System
![Page 6: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/6.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Introduction
Recommendation System
![Page 7: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/7.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Introduction
Recommendation System
![Page 8: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/8.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Recommender system
Memberikan rekomendasi, biasanya barang, kepada orang (user)Misalnya :
I Kamera digital mana yang harus saya beli?
I Akun twitter mana yang akan harus saya follow?
I Universitas mana yang tepat bagi saya?
Recommendation System
![Page 9: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/9.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Recommender system
Memberikan rekomendasi, biasanya barang, kepada orang (user)Misalnya :
I Kamera digital mana yang harus saya beli?
I Akun twitter mana yang akan harus saya follow?
I Universitas mana yang tepat bagi saya?
Recommendation System
![Page 10: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/10.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Recommender system
Memberikan rekomendasi, biasanya barang, kepada orang (user)Misalnya :
I Kamera digital mana yang harus saya beli?
I Akun twitter mana yang akan harus saya follow?
I Universitas mana yang tepat bagi saya?
Recommendation System
![Page 11: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/11.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Recommender system
Memberikan rekomendasi, biasanya barang, kepada orang (user)Misalnya :
I Kamera digital mana yang harus saya beli?
I Akun twitter mana yang akan harus saya follow?
I Universitas mana yang tepat bagi saya?
Recommendation System
![Page 12: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/12.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Recommender system
Kegunaan recommender system :
I Mempersempit information overload
I Kegunaan bagi user : mendapatkan hal yang menarik,mempersempit pilihan, menemukan hal yang baru
I Kegunaan bagi provider : memberikan rekomendasi yang lebihpersonal kepada user-nya, meningkatkan loyalitas user,meningkatkan pembelian, peluang untuk promosi,mendapatkan pengetahuan tentang user-nya
Recommendation System
![Page 13: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/13.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Recommender system
Kegunaan recommender system :
I Mempersempit information overload
I Kegunaan bagi user : mendapatkan hal yang menarik,mempersempit pilihan, menemukan hal yang baru
I Kegunaan bagi provider : memberikan rekomendasi yang lebihpersonal kepada user-nya, meningkatkan loyalitas user,meningkatkan pembelian, peluang untuk promosi,mendapatkan pengetahuan tentang user-nya
Recommendation System
![Page 14: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/14.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Recommender system
Kegunaan recommender system :
I Mempersempit information overload
I Kegunaan bagi user : mendapatkan hal yang menarik,mempersempit pilihan, menemukan hal yang baru
I Kegunaan bagi provider : memberikan rekomendasi yang lebihpersonal kepada user-nya, meningkatkan loyalitas user,meningkatkan pembelian, peluang untuk promosi,mendapatkan pengetahuan tentang user-nya
Recommendation System
![Page 15: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/15.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Introduction
Recommenders vs Search Engines
Kategori recommendation system
Input recommendation system
Collaborative Filtering
Content-based
Evaluasi recommendation system
Masalah dalam recommendation system
Recommendation System
![Page 16: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/16.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Recommenders vs Search Engines
I Search engines bukan sebuah sistem rekomendasi
I Query untuk mencari rekomendasi pada search enginemenghasilkan kumpulan sistem rekomendasi
Recommendation System
![Page 17: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/17.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Recommenders vs Search Engines
I Search engines bukan sebuah sistem rekomendasi
I Query untuk mencari rekomendasi pada search enginemenghasilkan kumpulan sistem rekomendasi
Recommendation System
![Page 18: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/18.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Recommenders vs Search Engines
Recommendation System
![Page 19: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/19.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Introduction
Recommenders vs Search Engines
Kategori recommendation system
Input recommendation system
Collaborative Filtering
Content-based
Evaluasi recommendation system
Masalah dalam recommendation system
Recommendation System
![Page 20: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/20.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Kategori Recommendation System
Content based filtering: ”Rekomendasikan buku yang sesuaidengan tipe buku yang saya suka”. Biasanyamenggunakan fitur barang.
Collaborative filtering: ”Rekomendasikan buku yang disukai olehteman-teman saya”. Menggunakan preferensikomunitas.
Hybrid: Kombinasi dari CF dan content-based
Recommendation System
![Page 21: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/21.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Kategori Recommendation System
Content based filtering: ”Rekomendasikan buku yang sesuaidengan tipe buku yang saya suka”. Biasanyamenggunakan fitur barang.
Collaborative filtering: ”Rekomendasikan buku yang disukai olehteman-teman saya”. Menggunakan preferensikomunitas.
Hybrid: Kombinasi dari CF dan content-based
Recommendation System
![Page 22: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/22.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Kategori Recommendation System
Content based filtering: ”Rekomendasikan buku yang sesuaidengan tipe buku yang saya suka”. Biasanyamenggunakan fitur barang.
Collaborative filtering: ”Rekomendasikan buku yang disukai olehteman-teman saya”. Menggunakan preferensikomunitas.
Hybrid: Kombinasi dari CF dan content-based
Recommendation System
![Page 23: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/23.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Introduction
Recommenders vs Search Engines
Kategori recommendation system
Input recommendation system
Collaborative Filtering
Content-based
Evaluasi recommendation system
Masalah dalam recommendation system
Recommendation System
![Page 24: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/24.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Input Recommendation System
(Vozalis & Margaritis, 2003) menyatakan input sistem rekomendasiada tiga:
I Demographic data : umur, jenis kelamin, dll
I Content data : analisis tekstual dari barang-barang yg pernahdibeli oleh user
I Ratings : scalar, binary, unary. Selain itu ada juga ratingimplisit dan eksplisit.
Recommendation System
![Page 25: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/25.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Input Recommendation System
(Vozalis & Margaritis, 2003) menyatakan input sistem rekomendasiada tiga:
I Demographic data : umur, jenis kelamin, dll
I Content data : analisis tekstual dari barang-barang yg pernahdibeli oleh user
I Ratings : scalar, binary, unary. Selain itu ada juga ratingimplisit dan eksplisit.
Recommendation System
![Page 26: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/26.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Input Recommendation System
(Vozalis & Margaritis, 2003) menyatakan input sistem rekomendasiada tiga:
I Demographic data : umur, jenis kelamin, dll
I Content data : analisis tekstual dari barang-barang yg pernahdibeli oleh user
I Ratings : scalar, binary, unary. Selain itu ada juga ratingimplisit dan eksplisit.
Recommendation System
![Page 27: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/27.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Input Recommendation System
(Vozalis & Margaritis, 2003) menyatakan input sistem rekomendasiada tiga:
I Demographic data : umur, jenis kelamin, dll
I Content data : analisis tekstual dari barang-barang yg pernahdibeli oleh user
I Ratings : scalar, binary, unary. Selain itu ada juga ratingimplisit dan eksplisit.
Recommendation System
![Page 28: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/28.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Introduction
Recommenders vs Search Engines
Kategori recommendation system
Input recommendation system
Collaborative Filtering
Content-based
Evaluasi recommendation system
Masalah dalam recommendation system
Recommendation System
![Page 29: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/29.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Collaborative Filtering
”wisdom of crowd”
Recommendation System
![Page 30: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/30.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
CF : Keuntungan
(Melville et al., 2002) menyebutkan dua keuntungan CF:
I bisa digunakan pada domains dimana di dalamnya terdapatcontent yangx tidak berhubungan dengan items
I serendipitious recommendations
Recommendation System
![Page 31: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/31.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
CF : Keuntungan
(Melville et al., 2002) menyebutkan dua keuntungan CF:
I bisa digunakan pada domains dimana di dalamnya terdapatcontent yangx tidak berhubungan dengan items
I serendipitious recommendations
Recommendation System
![Page 32: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/32.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
CF : Keuntungan
(Melville et al., 2002) menyebutkan dua keuntungan CF:
I bisa digunakan pada domains dimana di dalamnya terdapatcontent yangx tidak berhubungan dengan items
I serendipitious recommendations
Recommendation System
![Page 33: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/33.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
CF : InputInput dari CF : matriks user-item ratings
Recommendation System
![Page 34: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/34.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
CF : Output
Output dari CF : top-N list, prediksi rating score
Recommendation System
![Page 35: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/35.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
CF : Metode
Nearest Neighbour
I user-to-user
I item-to-item
Recommendation System
![Page 36: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/36.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
CF : Metode
Nearest Neighbour
I user-to-user
I item-to-item
Recommendation System
![Page 37: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/37.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
CF : User-to-user
Terdapat sebuah matriks berisi rating. Kira-kira berapa ratingAlice untuk item5?
Recommendation System
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Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
CF User-to-user : Pearson correlation coefficientBagaimana caranya mengukur kesamaan antar user?Solusi:
Pearson correlation coefficientsim(a, b) = ∑
p∈P(ra,p − ra)(rb,p − rb)√∑p∈P(ra,p − ra)2
√∑p∈P(rb,p − rb)2
Variables
I a, b: users
I P: items, yang sudah dirating oleh a dan b
I ra,p: rating dari user a untuk item p
Recommendation System
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Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
CF User-to-user : Menentukan prediksi
Weighted normalized adjusted average
pred(a, b) =
ra +
∑b∈N sim(a, b) ∗ (rb,p − rb)∑
b∈N sim(a, b)
Recommendation System
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Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
CF User-to-user : Menentukan prediksi
Lainnya :
I Simple average
I Weighted average
Recommendation System
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Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
CF User-to-user : Menentukan prediksi
Jika kita ambil 2-Nearest-Neighbour
PAlice,item5 = 3+42 = 3.5
Recommendation System
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Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
CF : Item-to-item
Memberikan prediksi berdasarkan kesamaan antar barang (items)
Recommendation System
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Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
CF Item-to-item : Cosine similarity
Kesamaan dihitung dengan cosine similarity (yang paling seringdigunakan), dimana ratings dianggap sebagai vektor
Cosine similarity
sim(−→a ,−→b ) =
−→a .−→b
|−→a |∗|−→b |
Recommendation System
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Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
CF Item-to-item : Cosine similarity
Opsi lain adalah adjusted cosine similarity yang menggunakanrata-rata ratings dari user untuk mengubah rating awal
Adjusted Cosine similarity
sim(−→a ,−→b ) = ∑
u∈U(ru,a − ru)(ru,b − ru)√∑u∈U(ru,a − ru)2
√∑u∈U(ru,b − ru)2
Variables
I U: satu set users yang sudah melakukan rating terhadapitems a dan b
Recommendation System
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Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
CF Item-to-item : Menentukan prediksi
Prediction functionpred(u, p) = ∑
i∈ratedItems(u) sim(i , p) ∗ ru,i∑i∈ratedItem(u) sim(i , p)
Recommendation System
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Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
CF Item-to-item : Menentukan prediksi
Recommendation System
![Page 47: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/47.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Introduction
Recommenders vs Search Engines
Kategori recommendation system
Input recommendation system
Collaborative Filtering
Content-based
Evaluasi recommendation system
Masalah dalam recommendation system
Recommendation System
![Page 48: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/48.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Content-based
I Membutuhkan informasi barang, tidak perlu informasikomunitas
I Dibutuhkan : informasi tentangg konten barang informasitentang apa yg disukai oleh user
I Biasanya dipakai untuk text-based, misalnya berita ¿¿ textclassification
Recommendation System
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Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Content-based
I Membutuhkan informasi barang, tidak perlu informasikomunitas
I Dibutuhkan : informasi tentangg konten barang informasitentang apa yg disukai oleh user
I Biasanya dipakai untuk text-based, misalnya berita ¿¿ textclassification
Recommendation System
![Page 50: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/50.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Content-based
I Membutuhkan informasi barang, tidak perlu informasikomunitas
I Dibutuhkan : informasi tentangg konten barang informasitentang apa yg disukai oleh user
I Biasanya dipakai untuk text-based, misalnya berita ¿¿ textclassification
Recommendation System
![Page 51: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/51.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Content-based : Keuntungan
Keuntungan dari content-based:
I komunitas tidak diperlukan
I lebih mudah daripada CF?
Recommendation System
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Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Content-based : Keuntungan
Keuntungan dari content-based:
I komunitas tidak diperlukan
I lebih mudah daripada CF?
Recommendation System
![Page 53: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/53.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Content-based : Keuntungan
Keuntungan dari content-based:
I komunitas tidak diperlukan
I lebih mudah daripada CF?
Recommendation System
![Page 54: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/54.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Content-based : tf-idf
Karena merupakan text classification maka digunakan tf-idf danclassifiers klasik seperti Naive Bayes dan SVM
Recommendation System
![Page 55: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/55.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Introduction
Recommenders vs Search Engines
Kategori recommendation system
Input recommendation system
Collaborative Filtering
Content-based
Evaluasi recommendation system
Masalah dalam recommendation system
Recommendation System
![Page 56: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/56.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Evaluasi Recommendation System
Statistical accuracy metrics: Mean Absolute Error (MAE), RootMean Squared Error (RMSE)
Decision support accuracy: biasanya untuk data binary,menunjukkan kualitas dari barang yangdirekomendasi : Precision, Recall, ROC
Recommendation System
![Page 57: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/57.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Evaluasi Recommendation System
Statistical accuracy metrics: Mean Absolute Error (MAE), RootMean Squared Error (RMSE)
Decision support accuracy: biasanya untuk data binary,menunjukkan kualitas dari barang yangdirekomendasi : Precision, Recall, ROC
Recommendation System
![Page 58: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/58.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Introduction
Recommenders vs Search Engines
Kategori recommendation system
Input recommendation system
Collaborative Filtering
Content-based
Evaluasi recommendation system
Masalah dalam recommendation system
Recommendation System
![Page 59: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/59.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Masalah dalam Recommendation System
Data sparsity Solusi? SVD (Singular Value Decomposition)
Cold start First rater problem
Shilling attacks Biased ratings
Recommendation System
![Page 60: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/60.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Masalah dalam Recommendation System
Data sparsity Solusi? SVD (Singular Value Decomposition)
Cold start First rater problem
Shilling attacks Biased ratings
Recommendation System
![Page 61: Recommendation System - kungfumas.files.wordpress.com · I Content data : analisis tekstual dari barang-barang yg pernah dibeli oleh user I Ratings : scalar, binary, unary. Selain](https://reader031.fdocuments.us/reader031/viewer/2022022618/5c88488e09d3f2224c8b6400/html5/thumbnails/61.jpg)
Introduction Recommenders vs Search Engines Kategori recommendation system Input recommendation system Collaborative Filtering Content-based Evaluasi recommendation system Masalah dalam recommendation system
Masalah dalam Recommendation System
Data sparsity Solusi? SVD (Singular Value Decomposition)
Cold start First rater problem
Shilling attacks Biased ratings
Recommendation System