python 新闻推荐系统(基于新闻内容推荐)

# -*- coding:utf-8 -*-__version__ = ‘1.0.0.0‘"""@brief  :   基于新闻的内容推荐系统@details:   详细信息@author :   zhphuang@date   :   2019-08-07"""import jieba

from pandas import *from sklearn.metrics import pairwise_distancesfrom bs4 import BeautifulSoupfrom sklearn import feature_extractionfrom sklearn.feature_extraction.text import TfidfTransformer, TfidfVectorizerfrom mysql_util import MySqlUtils

class ContentRecommend(object):

    def __init__(self):        DATABASE_CONFIG = {            "host": "127.0.0.1",            "port": 3306,            "user": "root",            "passwd": "root",            "db": "recommend",            "charset": "utf8",        }

        self.con = MySqlUtils(DATABASE_CONFIG)

        self.recommend_count = 10        self.all_count = 1000        self.reload_data()

    def reload_data(self):        corpus = []        sql = "select a.news_id,a.content,b.source_url as url from news_content as a left join news as b on a.news_id=b.news_id"        self.df = DataFrame(list(self.con.query(sql)), columns=[‘news_id‘, ‘content‘, ‘url‘])        zhPattern = re.compile(u‘[\u4e00-\u9fa5]+‘)        for index, row in self.df[0:self.all_count].iterrows():            print(index)            content = row[‘content‘]            bs = BeautifulSoup(content, "html.parser")            segments = []            segs = jieba.cut(bs.text)            for seg in segs:                if zhPattern.search(seg):                    segments.append(seg)            corpus.append(‘ ‘.join(segments))

        vectorizer = TfidfVectorizer()  # 该类会统计每个词语的tf-idf权值        tfidf = vectorizer.fit_transform(corpus)  # 第一个fit_transform是计算tf-idf，第二个fit_transform是将文本转为词频矩阵        words = vectorizer.get_feature_names()        print(len(words))        print(words)        print(vectorizer.vocabulary_)        a = tfidf.todense()        print(a)

        self.distance_matrix = pairwise_distances(            tfidf,            metric=‘cosine‘        )

    def analysis(self):        for index, item in enumerate(self.distance_matrix):            #min_value = np.min(np.delete(item, index))            #min_index = np.argmin(np.delete(item, index))            b = np.argsort(item)[1:self.recommend_count]            print("="*10 + "与%s相似的文章有:" % self.df.iloc[index, 0] + "="*10 )            for index_2 in b:                print(self.df.iloc[index_2, 0], "余弦距离:%s" % item[index_2])

    def get_similar_news_id(self, news_id):        for index, item in enumerate(self.distance_matrix):            if self.df.iloc[index, 0] == news_id:                b = np.argsort(item)[1:self.recommend_count]                print("="*10 + "与%s相似的文章有:" % self.df.iloc[index, 0] + "(链接地址:%s)" % self.df.iloc[index, 2] + "="*10 )                for index_2 in b:                    print(self.df.iloc[index_2, 0], "余弦相似度:%s" % item[index_2], "链接地址:%s" % self.df.iloc[index_2, 2])

    def get_recommend_news_by_uid(self, uid):        pass

    def input_news_id(self):        while 1:            print("输入newsId:")            news_id = input()            self.get_similar_news_id(news_id)

if __name__ == ‘__main__‘:    ContentRecommend().input_news_id()

原文地址：https://www.cnblogs.com/niuniuc/p/11314661.html