python图书推荐系统_图书推荐系统的实现含源码（协同过滤）

项目简介

基于模型的协同过滤应用---图书推荐

本文实现对用户推荐图书的简单应用。

推荐算法：

在我们的在线图书推荐系统中，我们借用Spark的ALS算法的训练和预测函数，每次收到新的数据后，将其更新到训练数据集中，然后更新ALS训练得到的模型。

假设我们有一组用户，他们表现出了对一组图书的喜好。用户对一本图书的喜好程度越高，就会给其更高的评分，范围是从1到5。我们来通过一个矩阵来展示它，行代表用户，列代表图书。用户对图书的评分。所有的评分范围从1到5，5代表喜欢程度最高。第一个用户(行1)对第一个图书(列1)的评分是4。空的单元格代表用户未给图书评价。

用户图书评分表.png

矩阵因子分解(如奇异值分解，奇异值分解+ +)将项和用户都转化成了相同的潜在空间，它所代表了用户和项之间的潜相互作用。矩阵分解背后的原理是潜在特征代表了用户如何给项进行评分。给定用户和项的潜在描述，我们可以预测用户将会给还未评价的项多少评分。

矩阵因子分解.png

数据描述：

评分数据文件:

"User-ID";"ISBN";"Book-Rating"

"276725";"034545104X";"0"

"276726";"0155061224";"5"

"276727";"0446520802";"0"

"276729";"052165615X";"3"

"276729";"0521795028";"6"

"276733";"2080674722";"0"

"276736";"3257224281";"8"

图书数据文件:

"ISBN";"Book-Title";"Book-Author";"Year-Of-Publication";"Publisher";"Image-URL-S";"Image-URL-M";"Image-URL-L"

"0195153448";"Classical Mythology";"Mark P. O. Morford";"2002";"Oxford University Press";"/images/P/0195153448.01.THUMBZZZ.jpg";"/images/P/0195153448.01.MZZZZZZZ.jpg";"/images/P/0195153448.01.LZZZZZZZ.jpg"

"000018";"Clara Callan";"Richard Bruce Wright";"2001";"HarperFlamingo Canada";"/images/P/000018.01.THUMBZZZ.jpg";"/images/P/000018.01.MZZZZZZZ.jpg";"/images/P/000018.01.LZZZZZZZ.jpg"

"0060973129";"Decision in Normandy";"Carlo D'Este";"1991";"HarperPerennial";"/images/P/0060973129.01.THUMBZZZ.jpg";"/images/P/0060973129.01.MZZZZZZZ.jpg";"/images/P/0060973129.01.LZZZZZZZ.jpg"

"0374157065";"Flu: The Story of the Great Influenza Pandemic of 1918 and the Search for the Virus That Caused It";"Gina Bari Kolata";"1999";"Farrar Straus Giroux";"/images/P/0374157065.01.THUMBZZZ.jpg";"/images/P/0374157065.01.MZZZZZZZ.jpg";"/images/P/0374157065.01.LZZZZZZZ.jpg"

"0393045218";"The Mummies of Urumchi";"E. J. W. Barber";"1999";"W. W. Norton & Company";"/images/P/0393045218.01.THUMBZZZ.jpg";"/images/P/0393045218.01.MZZZZZZZ.jpg";"/images/P/0393045218.01.LZZZZZZZ.jpg"

数据处理细节：

由于该数据中ISBN为string格式，spark的ALS默认product id为int格式，因此对该ISBN号进行计算hash处理并取前8位防止整数越界。详细代码如下：

dataset_path = os.path.join('datasets', 'BX-CSV-Dump')

sc = SparkContext("local[*]", "Test")

ratings_file_path = os.path.join(dataset_path, 'BX-Book-Ratings.csv')

ratings_raw_RDD = sc.textFile(ratings_file_path)

ratings_raw_data_header = ratings_raw_RDD.take(1)[0]

ratings_RDD = ratings_raw_RDD.filter(lambda line: line!=ratings_raw_data_header)\

.map(lambda line: line.split(";")).map(lambda tokens: (int(tokens[0][1:-1]), abs(hash(tokens[1][1:-1])) % (10 ** 8),float(tokens[2][1:-1]))).cache()

books_file_path = os.path.join(dataset_path, 'BX-Books.csv')

books_raw_RDD = sc.textFile(books_file_path)

books_raw_data_header = books_raw_RDD.take(1)[0]

books_RDD = books_raw_RDD.filter(lambda line: line!=books_raw_data_header)\

.map(lambda line: line.split(";"))\

.map(lambda tokens: (abs(hash(tokens[0][1:-1])) % (10 ** 8), tokens[1][1:-1], tokens[2][1:-1], tokens[3][1:-1], tokens[4][1:-1], tokens[5][1:-1])).cache()

books_titles_RDD = books_RDD.map(lambda x: (int(x[0]), x[1], x[2], x[3], x[4], x[5])).cache()

选择模型参数：

from pyspark.mllib.recommendation import ALS

import math

seed = 5L

iterations = 10

regularization_parameter = 0.1

ranks = [4, 8, 12]

errors = [0, 0, 0]

err = 0

tolerance = 0.02

min_error = float('inf')

best_rank = -1

best_iteration = -1

for rank in ranks:

model = ALS.train(training_RDD, rank, seed=seed, iterations=iterations,

lambda_=regularization_parameter)

predictions = model.predictAll(validation_for_predict_RDD).map(lambda r: ((r[0], r[1]), r[2]))

rates_and_preds = validation_RDD.map(lambda r: ((int(r[0]), int(r[1])), float(r[2]))).join(predictions)

error = math.sqrt(rates_and_preds.map(lambda r: (r[1][0] - r[1][1])**2).mean())

errors[err] = error

err += 1

print 'For rank %s the RMSE is %s' % (rank, error)

if error < min_error:

min_error = error

best_rank = rank

print 'The best model was trained with rank %s' % best_rank

模型保存

from pyspark.mllib.recommendation import MatrixFactorizationModel

model_path = os.path.join('..', 'models', 'book_als')

# Save and load model

model.save(sc, model_path)

same_model = MatrixFactorizationModel.load(sc, model_path)

运行说明：

virtualenv book

pip install -r requirements.txt

python server.py

API:

GET: //ratings/top/ 获取用户图书推荐top N信息

GET: //ratings/ 获取该用户对某个图书的评价信息

POST: //ratings 新增图书评价信息

接口调用示例：

GET: /276729/ratings/top/3 获取用户ID为276729的图书推荐top3信息

返回信息：

[

{

"Count": 30,

"Rating": 8.781754720405482,

"Author": "MARJANE SATRAPI",

"URL": "/images/P/0375422307.01.THUMBZZZ.jpg",

"Publisher": "Pantheon",

"Title": "Persepolis : The Story of a Childhood (Alex Awards (Awards))",

"Year": ""

},

{

"Count": 31,

"Rating": 7.093566643463471,

"Author": "Stephen King",

"URL": "/images/P/067081458X.01.THUMBZZZ.jpg",

"Publisher": "Viking Books",

"Title": "The Eyes of the Dragon",

"Year": "1987"

},

{

"Count": 25,

"Rating": 7.069147186199548,

"Author": "Jean Sasson",

"URL": "/images/P/0967673747.01.THUMBZZZ.jpg",

"Publisher": "Windsor-Brooke Books",

"Title": "Princess: A True Story of Life Behind the Veil in Saudi Arabia",

"Year": "2001"

}

]

GET: /276729/ratings/0446520802 获取用户276729对图书(ISBN:0446520802)的评价信息

返回信息：

[

{

"Count": 116,

"Rating": 1.4087434932956826,

"Author": "Nicholas Sparks",

"URL": "/images/P/0446520802.01.THUMBZZZ.jpg",

"Publisher": "Warner Books",

"Title": "The Notebook",

"Year": "1996"

}

]

以下数据可以提供给初学者学习如何训练推荐算法模型

电影数据:

MovieLens - Movie Recommendation Data Sets /node/73

Cornell University - Movie-review data for use in sentiment-analysis experiments http://www.cs.cornell.edu/people/pabo/movie-review-data/

音乐数据:

图书数据:

Institut für Informatik, Universität Freiburg - Book Ratings Data Sets http://www.informatik.uni-freiburg.de/~cziegler/BX/

美食数据:

商品数据:

Amazon - Product Recommendation Data Sets http://131.193.40.52/data/

健康数据:

相亲数据:

/petricek/data/

学术文章推荐:

National University of Singapore - Scholarly Paper Recommendation p.nus.edu.sg/~sugiyama/SchPaperRecData.html

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