Dekomposisi nilai singular (SVD)

Membangun Recommendation Engine di Python

Rob O'Callaghan

Director of Data

Apa yang dilakukan SVD

Matriks persegi panjang jarang

Membangun Recommendation Engine di Python

Apa yang dilakukan SVD

Matriks persegi panjang jarang dan faktor pertamanya

Membangun Recommendation Engine di Python

Apa yang dilakukan SVD

Matriks persegi panjang jarang dan dua faktornya

Membangun Recommendation Engine di Python

Apa yang dilakukan SVD

Matriks persegi panjang jarang dan faktor-faktornya

Membangun Recommendation Engine di Python

Menyiapkan data

print(book_ratings_df.shape)

(220, 500)

avg_ratings = book_ratings_df.mean(axis=1)

print(avg_ratings)

array([[4.5 ],
       [3.5],
       [2.5],
       [3.5],
        ... 
       [2.2]])
Membangun Recommendation Engine di Python

Menyiapkan data

user_ratings_pivot_centered = user_ratings_df.sub(avg_ratings, axis=0)
user_ratings_df.fillna(0, inplace=True)

print(user_ratings_df)

          The Great Gatsby    The Catcher in the Rye    Fifty Shades of Grey                    
User_233               0.0                       0.0                     0.0
User_651               0.0                       0.5                    -0.5
User_965               0.5                      -0.5                     0.0
     ...               ...                       ...                     ...
Membangun Recommendation Engine di Python

Menerapkan SVD

from scipy.sparse.linalg import svds

U, sigma, Vt = svds(user_ratings_pivot_centered)

print(U.shape)

(610, 6)

print(Vt.shape)

(6, 1000)
Membangun Recommendation Engine di Python

Menerapkan SVD

print(sigma)

[3.0, 4.8, -12.6, -3.8, 8.2, 7.3]

sigma = np.diag(sigma)
print(sigma)

array([   3.0    ,   0.     ,   0.     ,   0.     ,   0.     ,   0.     ],
       [  0.     ,   4.8    ,   0.     ,   0.     ,   0.     ,   0.     ],
       [  0.     ,   0.     , -12.6    ,   0.     ,   0.     ,   0.     ],
       [  0.     ,   0.     ,   0.     ,  -3.8    ,   0.     ,   0.     ],
       [  0.     ,   0.     ,   0.     ,   0.     ,   8.2    ,   0.     ],
       [  0.     ,   0.     ,   0.     ,   0.     ,   0.     ,   7.3    ]),
Membangun Recommendation Engine di Python

Mendapatkan matriks akhir

Tiga matriks faktor dari SVD

Membangun Recommendation Engine di Python

Mendapatkan matriks akhir

Tiga matriks faktor dari SVD

Membangun Recommendation Engine di Python

Mendapatkan matriks akhir

Tiga matriks faktor dari SVD

Membangun Recommendation Engine di Python

Mendapatkan matriks akhir

Tiga matriks faktor dari SVD di samping hasil perkaliannya

Membangun Recommendation Engine di Python

Menghitung produk di Python

recalculated_ratings =        np.dot(U, sigma)
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Menghitung produk di Python

recalculated_ratings = np.dot(np.dot(U, sigma), Vt)
print(recalculated_ratings)

[[  0.1      -0.9       -3.6.     ...   ]
 [ -2.3       0.5       -0.5      ...   ]
 [  0.5      -0.5        2.0      ...   ]
 [ ...        ...        ...      ...   ]]
Membangun Recommendation Engine di Python

Tambahkan rata-rata kembali

recalculated_ratings = recalculated_ratings + avg_ratings.values.reshape(-1, 1)
print(recalculated_ratings)

[[  4.6       3.6        0.9      ...   ]
 [  1.8       4.0        3.0      ...   ]
 [  3.0       2.0        4.5      ...   ]
 [ ...        ...        ...      ...   ]]

print(book_ratings_df)

[[  5.0       4.0         NA      ...   ]
 [   NA       4.0        3.0      ...   ]
 [  3.0       2.0         NA      ...   ]
 [ ...        ...        ...      ...   ]]
Membangun Recommendation Engine di Python

Ayo berlatih!

Membangun Recommendation Engine di Python

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