【机器学习】surprise推荐系统-电影推荐

要实现协同过滤，需要的步骤？

1.收集用户偏好
2.找到相似的用户或物品
3.计算推荐

基于用户的协同过滤 和基于物品的协同过滤比较

实例：

from surprise import KNNBasic,SVD
from surprise import Dataset
from surprise import evaluate, print_perf
# http://surprise.readthedocs.io/en/stable/index.html
# http://files.grouplens.org/datasets/movielens/ml-100k-README.txt

# Load the movielens-100k dataset (download it if needed),
# and split it into 3 folds for cross-validation.
data = Dataset.load_builtin('ml-100k')
data.split(n_folds=3)

# We'll use the famous KNNBasic algorithm.
algo = KNNBasic()

# Evaluate performances of our algorithm on the dataset.
perf = evaluate(algo, data, measures=['RMSE', 'MAE'])

print_perf(perf)

Evaluating RMSE, MAE of algorithm KNNBasic.

------------
Fold 1
Computing the msd similarity matrix...
Done computing similarity matrix.
RMSE: 0.9876
MAE:  0.7807
------------
Fold 2
Computing the msd similarity matrix...
Done computing similarity matrix.
RMSE: 0.9871
MAE:  0.7796
------------
Fold 3
Computing the msd similarity matrix...
Done computing similarity matrix.
RMSE: 0.9902
MAE:  0.7818
------------
------------
Mean RMSE: 0.9883
Mean MAE : 0.7807
------------
------------
        Fold 1  Fold 2  Fold 3  Mean    
MAE     0.7807  0.7796  0.7818  0.7807  
RMSE    0.9876  0.9871  0.9902  0.9883  

矩阵分解模型

from surprise import GridSearch

param_grid = {'n_epochs': [5, 10], 'lr_all': [0.002, 0.005],
              'reg_all': [0.4, 0.6]}
grid_search = GridSearch(SVD, param_grid, measures=['RMSE', 'FCP'])
data = Dataset.load_builtin('ml-100k')
data.split(n_folds=3)

grid_search.evaluate(data)

------------
Parameters combination 1 of 8
params:  {'lr_all': 0.002, 'n_epochs': 5, 'reg_all': 0.4}
------------
Mean RMSE: 0.9972
Mean FCP : 0.6843
------------
------------
Parameters combination 2 of 8
params:  {'lr_all': 0.005, 'n_epochs': 5, 'reg_all': 0.4}
------------
Mean RMSE: 0.9734
Mean FCP : 0.6946
------------
------------
Parameters combination 3 of 8
params:  {'lr_all': 0.002, 'n_epochs': 10, 'reg_all': 0.4}
------------
Mean RMSE: 0.9777
Mean FCP : 0.6926
------------
------------
Parameters combination 4 of 8
params:  {'lr_all': 0.005, 'n_epochs': 10, 'reg_all': 0.4}
------------
Mean RMSE: 0.9635
Mean FCP : 0.6987
------------
------------
Parameters combination 5 of 8
params:  {'lr_all': 0.002, 'n_epochs': 5, 'reg_all': 0.6}
------------
Mean RMSE: 1.0029
Mean FCP : 0.6875
------------
------------
Parameters combination 6 of 8
params:  {'lr_all': 0.005, 'n_epochs': 5, 'reg_all': 0.6}
------------
Mean RMSE: 0.9820
Mean FCP : 0.6953
------------
------------
Parameters combination 7 of 8
params:  {'lr_all': 0.002, 'n_epochs': 10, 'reg_all': 0.6}
------------
Mean RMSE: 0.9860
Mean FCP : 0.6943
------------
------------
Parameters combination 8 of 8
params:  {'lr_all': 0.005, 'n_epochs': 10, 'reg_all': 0.6}
------------
Mean RMSE: 0.9733
Mean FCP : 0.6991
------------

效果评估

import pandas as pd  

results_df = pd.DataFrame.from_dict(grid_search.cv_results)
results_df

基于item的协同过滤

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)
import os
import io

from surprise import KNNBaseline
from surprise import Dataset


def read_item_names():


    file_name = ('./ml-100k/u.item')
    rid_to_name = {}
    name_to_rid = {}
    with io.open(file_name, 'r', encoding='ISO-8859-1') as f:
        for line in f:
            line = line.split('|')
            rid_to_name[line[0]] = line[1]
            name_to_rid[line[1]] = line[0]

    return rid_to_name, name_to_rid



data = Dataset.load_builtin('ml-100k')
trainset = data.build_full_trainset()
sim_options = {'name': 'pearson_baseline', 'user_based': False}
algo = KNNBaseline(sim_options=sim_options)
algo.train(trainset)

推荐用户喜欢的10部电影

toy_story_neighbors = algo.get_neighbors(toy_story_inner_id, k=10)
toy_story_neighbors

toy_story_neighbors = (algo.trainset.to_raw_iid(inner_id)
                       for inner_id in toy_story_neighbors)
toy_story_neighbors = (rid_to_name[rid]
                       for rid in toy_story_neighbors)

print()
print('The 10 nearest neighbors of Toy Story are:')
for movie in toy_story_neighbors:
    print(movie)

The 10 nearest neighbors of Toy Story are:
While You Were Sleeping (1995)
Batman (1989)
Dave (1993)
Mrs. Doubtfire (1993)
Groundhog Day (1993)
Raiders of the Lost Ark (1981)
Maverick (1994)
French Kiss (1995)
Stand by Me (1986)
Net, The (1995)