神級程式設計師帶來的：基於Python和Tensorflow的電影推薦演算法！
阿新 • • 發佈：2019-01-03
| userId | movieId | rating | timestamp | |
|---|---|---|---|---|
| 99999 | 671 | 6268 | 2.5 | 1065579370 |
| 100000 | 671 | 6269 | 4.0 | 1065149201 |
| 100001 | 671 | 6365 | 4.0 | 1070940363 |
| 100002 | 671 | 6385 | 2.5 | 1070979663 |
| 100003 | 671 | 6565 | 3.5 | 1074784724 |
| movieId | title | genres | |
|---|---|---|---|
| 9120 | 162672 | Mohenjo Daro (2016) | Adventure|Drama|Romance |
| 9121 | 163056 | Shin Godzilla (2016) | Action|Adventure|Fantasy|Sci-Fi |
| 9122 | 163949 | The Beatles: Eight Days a Week - The Touring Y... | Documentary |
| 9123 | 164977 | The Gay Desperado (1936) | Comedy |
| 9124 | 164979 | Women of '69, Unboxed | Documentary |
| movieId | title | genres | movieRow | |
|---|---|---|---|---|
| 9120 | 162672 | Mohenjo Daro (2016) | Adventure|Drama|Romance | 9120 |
| 9121 | 163056 | Shin Godzilla (2016) | Action|Adventure|Fantasy|Sci-Fi | 9121 |
| 9122 | 163949 | The Beatles: Eight Days a Week - The Touring Y... | Documentary | 9122 |
| 9123 | 164977 | The Gay Desperado (1936) | Comedy | 9123 |
| 9124 | 164979 | Women of '69, Unboxed | Documentary | 9124 |
| movieRow | movieId | title | |
|---|---|---|---|
| 9120 | 9120 | 162672 | Mohenjo Daro (2016) |
| 9121 | 9121 | 163056 | Shin Godzilla (2016) |
| 9122 | 9122 | 163949 | The Beatles: Eight Days a Week - The Touring Y... |
| 9123 | 9123 | 164977 | The Gay Desperado (1936) |
| 9124 | 9124 | 164979 | Women of '69, Unboxed |
| userId | movieRow | rating | |
|---|---|---|---|
| 0 | 1 | 30 | 2.5 |
| 1 | 7 | 30 | 3.0 |
| 2 | 31 | 30 | 4.0 |
| 3 | 32 | 30 | 4.0 |
| 4 | 36 | 30 | 3.0 |
第三步：構建模型
loss = 1/2 * tf.reduce_sum(((tf.matmul(X_parameters, Theta_parameters, transpose_b = True) - rating_norm) * record) ** 2) + 1/2 * (tf.reduce_sum(X_parameters ** 2) + tf.reduce_sum(Theta_parameters ** 2))#基於內容的推薦演算法模型函式解釋：
reduce_sum() 就是求和，reduce_sum( input_tensor, axis=None, keep_dims=False, name=None, reduction_indices=None)
reduce_sum() 引數解釋：
檢視訓練結果
在終端輸入 tensorboard --logir=./
第五步：評估模型
4037.9002717628305第六步：構建完整的電影推薦系統
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