python推荐算法代码

下面是一个使用Python实现的推荐算法的代码示例： ```python import pandas as pd from surprise import Dataset from surprise import Reader from surprise import KNNBasic # 读取评分数据 heads=['user_id','item_id','rating','timestamp'] ratings = pd.read_csv('u.data', sep='\t', names=heads) # 创建Surprise所需的数据集 reader = Reader(rating_scale=(1, 5)) data = Dataset.load_from_df(ratings[['user_id', 'item_id', 'rating']], reader) # 构建基于用户的协同过滤模型 sim_options = {'name': 'cosine', 'user_based': True} model = KNNBasic(sim_options=sim_options) trainset = data.build_full_trainset() model.fit(trainset) # 进行推荐 user_id = 1 # 需要为哪个用户进行推荐 n_recommendations = 5 # 推荐的电影数量 # 获取用户未评分的电影列表 rated_movies = ratings[ratings['user_id'] == user_id]['item_id'] unrated_movies = ratings<span class="em">1</span><span class="em">2</span><span class="em">3</span> #### 引用[.reference_title] - *1* *2* [python协同过滤算法实现电影推荐（附源码）](https://blog.csdn.net/jiebaoshayebuhui/article/details/126966927)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 50%"] - *3* [指针式仪表倾斜校正opencv算法python代码及仪表图像（包含倾斜的和模板图像）](https://download.csdn.net/download/sunnyrainflower/88221223)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 50%"] [ .reference_list ]