开发技术
协同过滤算法、机器学习、LSTM、vue.js、echarts、django、Python、MySQL

创新点
协同过滤推荐算法、爬虫、数据可视化、LSTM情感分析、短信、身份证识别

补充说明
适合大数据毕业设计、数据分析、爬虫类计算机毕业设计

介绍

• 音乐数据的爬取：爬取歌曲、歌手、歌词、评论
• 音乐数据的可视化：数据大屏+多种分析图【十几个图】
• 深度学习之LSTM 音乐评论情感分析
• 交互式协同过滤音乐推荐： 2种协同过滤算法、通过点击歌曲喜欢来修改用户对歌曲的评分
• 歌词、乐评的词云
• 登录、注册、修改个人信息等【集成身份证识别、短信验证码等】

核心算法代码分享如下：

# coding = utf-8

# 基于项目的协同过滤推荐算法实现
import random

import math
import pymysql                          #数据库
from operator import itemgetter

from config import cnn


class ItemBasedCF():
    # 初始化参数
    def __init__(self):
        # 找到相似的8个，为目标用户推荐4个
        self.n_sim_movie = 8
        self.n_rec_movie = 4

        # 将数据集划分为训练集和测试集
        self.trainSet = {}
        self.testSet = {}

        # 用户相似度矩阵
        self.movie_sim_matrix = {}
        self.movie_popular = {}
        self.movie_count = 0

        print('Similar movie number = %d' % self.n_sim_movie)
        print('Recommneded movie number = %d' % self.n_rec_movie)

    # 从数据库得到“用户-物品”数据
    def get_dataset(self, pivot=0.75):
        trainSet_len = 0
        testSet_len = 0
        cnn.ping(reconnect=True)
        cursor = cnn.cursor()
        sql = ' select * from tb_rate'
        cursor.execute(sql)
        for item in cursor.fetchall():
            user, movie, rating = item[1:]
            self.trainSet.setdefault(user, {})
            self.trainSet[user][movie] = rating
            trainSet_len += 1
            self.testSet.setdefault(user, {})
            self.testSet[user][movie] = rating
            testSet_len += 1
        cursor.close()
        # cnn.close()

        print('Split trainingSet and testSet success!')
        print('TrainSet = %s' % trainSet_len)
        print('TestSet = %s' % testSet_len)

    # 读文件，返回文件的每一行
    def load_file(self, filename):
        with open(filename, 'r') as f:
            for i, line in enumerate(f):
                if i == 0:  # 去掉文件第一行的title
                    continue
                yield line.strip('\r\n')
        print('Load %s success!' % filename)

    # 计算物品之间的相似度
    def calc_movie_sim(self):
        for user, movies in self.trainSet.items():
            for movie in movies:
                if movie not in self.movie_popular:
                    self.movie_popular[movie] = 0
                self.movie_popular[movie] += 1

        self.movie_count = len(self.movie_popular)
        print("Total movie number = %d" % self.movie_count)

        for user, movies in self.trainSet.items():
            for m1 in movies:
                for m2 in movies:
                    if m1 == m2:
                        continue
                    self.movie_sim_matrix.setdefault(m1, {})
                    self.movie_sim_matrix[m1].setdefault(m2, 0)
                    self.movie_sim_matrix[m1][m2] += 1
        print("Build co-rated users matrix success!")

        # 计算物品之间的相似性 similarity matrix
        print("Calculating movie similarity matrix ...")
        for m1, related_movies in self.movie_sim_matrix.items():
            for m2, count in related_movies.items():
                # 注意0向量的处理，即某物品的用户数为0
                if self.movie_popular[m1] == 0 or self.movie_popular[m2] == 0:
                    self.movie_sim_matrix[m1][m2] = 0
                else:
                    self.movie_sim_matrix[m1][m2] = count / math.sqrt(self.movie_popular[m1] * self.movie_popular[m2])
        print('Calculate movie similarity matrix success!')

    # 针对目标用户U，找到K部相似的物品，并推荐其N部物品
    def recommend(self, user):
        K = self.n_sim_movie
        N = self.n_rec_movie
        rank = {}
        if user>len(self.trainSet):
            user = random.randint(1, len(self.trainSet))
        watched_movies = self.trainSet[user]

        for movie, rating in watched_movies.items():
            for related_movie, w in sorted(self.movie_sim_matrix[movie].items(), key=itemgetter(1), reverse=True)[:K]:
                if related_movie in watched_movies:
                    continue
                rank.setdefault(related_movie, 0)
                rank[related_movie] += w * float(rating)
        return sorted(rank.items(), key=itemgetter(1), reverse=True)[:N]

    # 产生推荐并通过准确率、召回率和覆盖率进行评估
    def evaluate(self):
        print('Evaluating start ...')
        N = self.n_rec_movie
        # 准确率和召回率
        hit = 0
        rec_count = 0
        test_count = 0
        # 覆盖率
        all_rec_movies = set()

        for i, user in enumerate(self.trainSet):
            test_moives = self.testSet.get(user, {})
            rec_movies = self.recommend(user)
            for movie, w in rec_movies:
                if movie in test_moives:
                    hit += 1
                all_rec_movies.add(movie)
            rec_count += N
            test_count += len(test_moives)

        precision = hit / (1.0 * rec_count)
        recall = hit / (1.0 * test_count)
        coverage = len(all_rec_movies) / (1.0 * self.movie_count)
        print('precisioin=%.4f\trecall=%.4f\tcoverage=%.4f' % (precision, recall, coverage))

    def rec_one(self,userId):
        print('推荐一个')
        rec_movies = self.recommend(userId)
        # print(rec_movies)
        return rec_movies

# itemCF 推荐算法接口
def recommend(userId):
    itemCF = ItemBasedCF()
    itemCF.get_dataset()
    itemCF.calc_movie_sim()
    reclist = []
    recs = itemCF.rec_one(userId)
    return recs
    # for movie, rate in recs:
    #     # print(movie, rate)
    #     reclist.append(dict(item=movie, rate=rate))
    # # itemCF.evaluate()
    # return reclist

# 测试
if __name__ == '__main__':
    print(recommend(1))