ML之CF:基于MovieLens电影评分数据集利用基于用户协同过滤算法(余弦相似度)实现对用户进行Top5电影推荐案例

ML之CF:基于MovieLens电影评分数据集利用基于用户协同过滤算法(余弦相似度)实现对用户进行Top5电影推荐案例

目录

基于MovieLens电影评分数据集利用基于用户协同过滤算法(余弦相似度)实现对用户进行Top5电影推荐案例

 # 1、定义数据集

# 3、模型训练与推理

# 3.1、切分数据集:将数据集分为训练集和测试集

# 3.2、文本数据集再处理

# 构建用户-电影评分矩阵

# 3.3、计算用户之间的相似度:余弦相似度

# 3.4、模型评估:计算准确率和召回率

# 3.5、模型推理:为用户1推荐电影


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ML之CF:基于MovieLens电影评分数据集利用基于用户协同过滤算法(余弦相似度)实现对用户进行Top5电影推荐案例
ML之CF:基于MovieLens电影评分数据集利用基于用户协同过滤算法(余弦相似度)实现对用户进行Top5电影推荐案例实现代码

基于MovieLens电影评分数据集利用基于用户协同过滤算法(余弦相似度)实现对用户进行Top5电影推荐案例

 # 1、定义数据集

userIdmovieIdratingtimestamp
114964982703
134964981247
164964982224
1475964983815
1505964982931
1703964982400
11015964980868
11104964982176
11515964984041
11575964984100

movieIdtitlegenres
1Toy Story (1995)Adventure|Animation|Children|Comedy|Fantasy
2Jumanji (1995)Adventure|Children|Fantasy
3Grumpier Old Men (1995)Comedy|Romance
4Waiting to Exhale (1995)Comedy|Drama|Romance
5Father of the Bride Part II (1995)Comedy
6Heat (1995)Action|Crime|Thriller
7Sabrina (1995)Comedy|Romance
8Tom and Huck (1995)Adventure|Children
9Sudden Death (1995)Action
10GoldenEye (1995)Action|Adventure|Thriller
11American President, The (1995)Comedy|Drama|Romance
userId movieId rating timestamp
0 1 1 4.0 964982703
1 1 3 4.0 964981247
2 1 6 4.0 964982224
3 1 47 5.0 964983815
4 1 50 5.0 964982931
... ... ... ... ...
100831 610 166534 4.0 1493848402
100832 610 168248 5.0 1493850091
100833 610 168250 5.0 1494273047
100834 610 168252 5.0 1493846352
100835 610 170875 3.0 1493846415
[100836 rows x 4 columns]

# 3、模型训练与推理

# 3.1、切分数据集:将数据集分为训练集和测试集

# 3.2、文本数据集再处理

# 构建用户-电影评分矩阵

train_matrix 
 movieId 1 2 3 4 ... 193583 193585 193587 193609
userId ... 
1 4.0 NaN 4.0 NaN ... NaN NaN NaN NaN
2 NaN NaN NaN NaN ... NaN NaN NaN NaN
3 NaN NaN NaN NaN ... NaN NaN NaN NaN
4 NaN NaN NaN NaN ... NaN NaN NaN NaN
5 NaN NaN NaN NaN ... NaN NaN NaN NaN
... ... ... ... ... ... ... ... ... ...
606 2.5 NaN NaN NaN ... NaN NaN NaN NaN
607 4.0 NaN NaN NaN ... NaN NaN NaN NaN
608 2.5 2.0 NaN NaN ... NaN NaN NaN NaN
609 3.0 NaN NaN NaN ... NaN NaN NaN NaN
610 NaN NaN NaN NaN ... NaN NaN NaN NaN
[610 rows x 8975 columns]

# 3.3、计算用户之间的相似度:余弦相似度

user_similarity 
 userId 1 2 3 4 5 6 7 8 9 ... 602 603 604 605 606 607 608 609 610
userId ... 
1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
2 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
3 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
4 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
5 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
... .. .. .. .. .. .. .. .. .. ... .. .. .. .. .. .. .. .. ..
606 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
607 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
608 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
609 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
610 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
[610 rows x 610 columns]

# 3.4、模型评估:计算准确率和召回率

userId movieId rating
618 408 138036 5.0
123 1 2459 5.0
650 409 1234 5.0
162 1 3273 5.0
163 1 3386 5.0
precision: 0.026973684210526316
recall: 0.004065846886156287

# 3.5、模型推理:为用户1推荐电影

userId movieId rating
618 408 138036 5.0
123 1 2459 5.0
650 409 1234 5.0
162 1 3273 5.0
163 1 3386 5.0
precision: 0.026973684210526316
recall: 0.004065846886156287
 userId movieId rating
460 405 32587 5.0
715 409 3814 5.0
286 410 3855 5.0
288 410 3910 5.0
487 406 56949 5.0

作者:一个处女座的程序猿原文地址:https://blog.csdn.net/qq_41185868/article/details/129700868

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