import csv
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

def tf_idf(items, user):
    # 数据载入
    articles = items
    df = pd.DataFrame(articles)
    # 提取TF-IDF特征
    tfidf_vectorizer = TfidfVectorizer()
    tfidf_matrix = tfidf_vectorizer.fit_transform(df['content'])
    # 计算余弦相似度
    cosine_sim = cosine_similarity(tfidf_matrix, tfidf_matrix)
    # 根据相似度推荐
    def recommend_articles(article_id, user, cosine_sim=cosine_sim):
        sim_scores = list(enumerate(cosine_sim[article_id - 1]))
        sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
        sim_scores = sim_scores[1:6]  # 推荐前5个相似文章
        article_indices = [i[0] for i in sim_scores]
        with open('collections/{}.csv'.format(user), 'a+', encoding='utf-8',newline='') as f:
            write=csv.DictWriter(f,fieldnames=['文章ID','content','时间','收藏数量','喜欢数量','图片资源'])
            for j in article_indices:
                write.writerow(items[j])

    # 推荐与文章ID为1的旅游攻略相似的文章
    recommend_articles(1,user)