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。

Image of Example1

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。

Image of Example1

  • 数据描述：

评分数据文件:

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

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"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"

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"0195153448";"Classical Mythology";"Mark P. O. Morford";"2002";"Oxford University Press";"http://images.amazon.com/images/P/0195153448.01.THUMBZZZ.jpg";"http://images.amazon.com/images/P/0195153448.01.MZZZZZZZ.jpg";"http://images.amazon.com/images/P/0195153448.01.LZZZZZZZ.jpg"
"0002005018";"Clara Callan";"Richard Bruce Wright";"2001";"HarperFlamingo Canada";"http://images.amazon.com/images/P/0002005018.01.THUMBZZZ.jpg";"http://images.amazon.com/images/P/0002005018.01.MZZZZZZZ.jpg";"http://images.amazon.com/images/P/0002005018.01.LZZZZZZZ.jpg"
"0060973129";"Decision in Normandy";"Carlo D'Este";"1991";"HarperPerennial";"http://images.amazon.com/images/P/0060973129.01.THUMBZZZ.jpg";"http://images.amazon.com/images/P/0060973129.01.MZZZZZZZ.jpg";"http://images.amazon.com/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";"http://images.amazon.com/images/P/0374157065.01.THUMBZZZ.jpg";"http://images.amazon.com/images/P/0374157065.01.MZZZZZZZ.jpg";"http://images.amazon.com/images/P/0374157065.01.LZZZZZZZ.jpg"
"0393045218";"The Mummies of Urumchi";"E. J. W. Barber";"1999";"W. W. Norton & Company";"http://images.amazon.com/images/P/0393045218.01.THUMBZZZ.jpg";"http://images.amazon.com/images/P/0393045218.01.MZZZZZZZ.jpg";"http://images.amazon.com/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：

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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：
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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
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from pyspark.mllib.recommendation import MatrixFactorizationModel
model_path = os.path.join('..', 'models', 'book_als')
model.save(sc, model_path)
same_model = MatrixFactorizationModel.load(sc, model_path)

  • 运行说明：

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    virtualenv book
    pip install -r requirements.txt
    python server.py

  • RESTful API:

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GET: /<int:user_id>/ratings/top/<int:count> 获取用户图书推荐top N信息
GET: /<int:user_id>/ratings/<string:book_id> 获取该用户对某个图书的评价信息
POST: /<int:user_id>/ratings 新增图书评价信息
  • 接口调用示例：
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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": "http://images.amazon.com/images/P/0375422307.01.THUMBZZZ.jpg",
    "Publisher": "Pantheon",
    "Title": "Persepolis : The Story of a Childhood (Alex Awards (Awards))",
    "Year": "2003"
  },
  {
    "Count": 31,
    "Rating": 7.093566643463471,
    "Author": "Stephen King",
    "URL": "http://images.amazon.com/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": "http://images.amazon.com/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"
  }
]
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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": "http://images.amazon.com/images/P/0446520802.01.THUMBZZZ.jpg",
    "Publisher": "Warner Books",
    "Title": "The Notebook",
    "Year": "1996"
  }
]

其他数据集推荐（参考https://gist.github.com/entaroadun/1653794）

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:

音乐数据:

图书数据:

美食数据:

商品数据:

健康数据:

相亲数据:

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