Saran non-personal
Membangun Recommendation Engine di Python
Robert O'Callaghan
Director of Data
Mengidentifikasi pasangan
0 User_223 The Great Gatsby <---| Dibaca oleh pengguna yang sama
1 User_223 The Catcher in the Rye <---|
2 User_131 The Lord of the Rings
3 User_965 Little Women <---| Dibaca oleh pengguna yang sama
4 User_965 Fifty Shades of Grey <---|
... ...
Permutasi vs kombinasi
| Pengguna | book_title |
|---|---|
| User_233 | The Great Gatsby |
| User_233 | The Catcher in the Rye |
Menjadi:
| Book A | Book B | |
|---|---|---|
| 0 | The Great Gatsby | The Catcher in the Rye |
| 1 | The Catcher in the Rye | The Great Gatsby |
Buku yang muncul bersama The Great Gatsby -> The Catcher in the Rye
Buku yang muncul bersama The Catcher in the Rye -> The Great Gatsby
Membuat fungsi penggandengan
from itertools import permutations
def create_pairs(x):
return pairs
Membuat fungsi penggandengan
from itertools import permutations
def create_pairs(x):
pairs = permutations(x.values, 2)
return pairs
permutations(list, length_of_permutations))Membuat objek iterable berisi semua permutasi
Membuat fungsi penggandengan
from itertools import permutations
def create_pairs(x):
pairs = list(permutations(x.values, 2))
return pairs
permutations(list, length_of_permutations))Membuat objek iterable berisi semua permutasilist()Mengonversi objek ini menjadi list yang dapat dipakai
Membuat fungsi penggandengan
from itertools import permutations
def create_pairs(x):
pairs = pd.DataFrame(list(permutations(x.values, 2)),
columns=['book_a','book_b'])
return pairs
permutations(list, length_of_permutations))Membuat objek iterable berisi semua permutasilist()Mengonversi objek ini menjadi list yang dapat dipakaipd.DataFrame()Mengonversi list menjadi DataFrame dengan kolombook_adanbook_b
Menerapkan fungsi ke data
book_pairs = book_df.groupby('userId')['book_title'].apply(perm_function)
print(book_pairs.head())
book_a book_b
userId
User_223 0 The Great Gatsby The Catcher in the Rye
1 The Catcher in the Rye The Great Gatsby
User_965 0 Little Women 40 Shades of Grey
1 40 Shades of Grey Little Women
User_773 0 The Twilight Saga Harry Potter and the Sorcerer's Stone
...
Merapikan hasil
book_pairs = book_pairs.reset_index(drop=True)
print(book_pairs.head())
book_a book_b
0 The Great Gatsby The Catcher in the Rye
1 The Catcher in the Rye The Great Gatsby
3 Little Women 40 Shades of Grey
4 40 Shades of Grey Little Women
5 The Twilight Saga Harry Potter and the Sorcerer's Stone
...
Menghitung pasangan
pair_counts = book_pairs.groupby(['book_a', 'book_b']).size()
book_a book_b
The Twilight Saga Fifty Shades of Grey 16
Pride and Prejudice 12
...
pair_counts_df = pair_counts.to_frame(name = 'size').reset_index()
print(pair_counts_df.head())
book_a book_b size
1 The Twilight Saga Fifty Shades of Grey 16
2 The Twilight Saga Pride and Prejudice 12
...
Mencari rekomendasi
pair_counts_sorted = pair_counts_df.sort_values('size', ascending=False)
pair_counts_sorted[pair_counts_sorted['book_a'] == 'Lord of the Rings']
book_a book_b size
137 Lord of the Rings The Hobbit 12
147 Lord of the Rings Harry Potter and the Sorcerer's Stone 10
143 Lord of the Rings The Colour of Magic 9
...
Ayo berlatih!
Membangun Recommendation Engine di Python
