推荐系统（二）：使用Tensorflow构造隐语义模型 —— MovieLens电影评分

版权声明：本文为博主原创文章，未经博主允许不得转载。

---

---

一、理论知识

1.1 论文

• 论文题目：《Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model 》
• 发表时间：KDD 2008
• 论文作者及单位：Yehuda Koren （AT&T Labs – Research）
• 论文地址：https://dl.acm.org/citation.cfm?id=1401944&preflayout=flat
• 论文译文：待整理

1.2 理解

① 均值、用户偏差、电影偏差
② 例如：

二、数据简介

• 推荐系统研究中常用的九大数据集
• 推荐系统数据中的几个类别：
  Item： 即我们要推荐的东西，如产品、电影、网页或者一条信息片段
  User：对item进行评分以及接受推荐系统推荐的项目的人
  Rating：用户对item的偏好的表达。评分可以是二分类的（如喜欢和不喜欢），也可以是整数（如1到5星）或连续（某个间隔的任何值）。 另外，还有一些隐反馈，只记录一个用户是否与一个项目进行了交互。

2.1 描述

These files contain 1,000,209 anonymous ratings of approximately 3,900 movies made by 6,040 MovieLens users who joined MovieLens in 2000.

1.ratings file description：ratings.dat

UserID	MovieID	Rating	Timestamp
1	1193	5	978300760
1	661	3	978302109
…	…	…	…

• UserIDs range between 1 and 6040
• MovieIDs range between 1 and 3952
• Ratings are made on a 5-star scale (whole-star ratings only)
• Timestamp is represented in seconds since the epoch as returned by time(2)
• Each user has at least 20 ratings

2.users file description：users.dat

UserID	Gender	Age	Occupation	Zip-code
1	F	1	10	48067
2	M	56	16	70072
…	…	…	…	…

• Gender is denoted by a “M” for male and “F” for female
• Age is chosen from the following ranges：
       * 1: “Under 18”
       * 18: “18-24”
       * 25: “25-34”
       * 35: “35-44”
       * 45: “45-49”
       * 50: “50-55”
       * 56: “56+”
• Occupation is chosen from the following choices：
       * 0: “other” or not specified
       * 1: “academic/educator”
       * 2: “artist”
       * 3: “clerical/admin”
       * 4: “college/grad student”
       * 5: “customer service”
       * 6: “doctor/health care”
       * 7: “executive/managerial”
       * 8: “farmer”
       * 9: “homemaker”
       * 10: “K-12 student”
       * 11: “lawyer”
       * 12: “programmer”
       * 13: “retired”
       * 14: “sales/marketing”
       * 15: “scientist”
       * 16: “self-employed”
       * 17: “technician/engineer”
       * 18: “tradesman/craftsman”
       * 19: “unemployed”
       * 20: “writer”

3.movies file description：movies.dat

MovieID	Title	Genres
1	Toy Story (1995)	Animation
2	Jumanji (1995)	Adventure
…	…	…

• Titles are identical to titles provided by the IMDB (includingyear of release)
• Genres are pipe-separated and are selected from the following genres：
       * Action
       * Adventure
       * Animation
       * Children’s
       * Comedy
       * Crime
       * Documentary
       * Drama
       * Fantasy
       * Film-Noir
       * Horror
       * Musical
       * Mystery
       * Romance
       * Sci-Fi
       * Thriller
       * War
       * Western
• Some MovieIDs do not correspond to a movie due to accidental duplicate entries and/or test entries
• Movies are mostly entered by hand, so errors and inconsistencies may exist

三、代码实现

3.1 数据介绍

• 3900个电影 6040个用户
• 数据简介：http://files.grouplens.org/datasets/movielens/ml-1m-README.txt
• 数据下载地址：http://files.grouplens.org/datasets/movielens/ml-1m.zip
• tensorflow下载地址：http://www.lfd.uci.edu/~gohlke/pythonlibs/#tensorflow

# Imports for data io operations
from collections import deque
from six import next
# 调用 reader.py
import reader

# Main imports for training
import tensorflow as tf
import numpy as np

# Evaluate train times per epoch
import time

# Constant seed for replicating training results
np.random.seed(42)

# Number of users in the dataset
u_num = 6040

# Number of movies in the dataset
i_num = 3952

# Number of samples per batch
batch_size = 1000

# Dimensions of the data, 15
dims = 5

# Number of times the network sees all the training data
max_epochs = 50

# Device used for all computations
place_device = '/cpu:0'

3.2 加载数据、划分数据集和测试集

def