基于python实现的音乐推荐系统源码(课程作业项目).zip

import numpy as np import pandas import math # 基于流行度的推荐模型 class popularity_recommender_py(): def __init__(self): self.train_data = None self.user_id = None self.item_id = None self.popularity_recommendations = None # 创建基于流行度的推荐模型 def create(self, train_data, user_id, item_id): self.train_data = train_data self.user_id = user_id self.item_id = item_id # 获取每个item的播放量，作为推荐指标 train_data_grouped = train_data.groupby([self.item_id]).agg({self.user_id: 'count'}).reset_index() #为了直观展示，用得分表示结果 train_data_grouped.rename(columns={user_id: 'score'}, inplace=True) #根据得分给歌曲排序 train_data_sort = train_data_grouped.sort_values(['score', self.item_id], ascending=[0, 1]) #加入一项排行等级，表示其推荐的优先级 train_data_sort['Rank'] = train_data_sort['score'].rank(ascending=0, method='first') # 得到top10列表 self.popularity_recommendations = train_data_sort.head(10) #使用基于流行度的算法进行推荐 def recommend(self, user_id): user_recommendations = self.popularity_recommendations #根据用户id为指定用户生成推荐列表 user_recommendations['user_id'] = user_id cols = user_recommendations.columns.tolist() cols = cols[-1:] + cols[:-1] user_recommendations = user_recommendations[cols] return user_recommendations #基于项目的协同过滤推荐 class item_similarity_recommender_py(): def __init__(self): self.train_data = None self.user_id = None self.item_id = None self.cooccurence_matrix = None self.songs_dict = None self.rev_songs_dict = None self.item_similarity_recommendations = None #给定用户，找出用户听过的所有歌曲 def get_user_items(self, user): user_data = self.train_data[self.train_data[self.user_id] == user] user_items = list(user_data[self.item_id].unique()) return user_items #给定歌曲，找出听过这首歌的所有用户 def get_item_users(self, item): item_data = self.train_data[self.train_data[self.item_id] == item] item_users = set(item_data[self.user_id].unique()) return item_users #对数据集中的歌曲去重 def get_all_items_train_data(self): all_items = list(self.train_data[self.item_id].unique()) return all_items #Jaccard系数构建相似度矩阵 def construct_cooccurence_matrix(self, user_songs, all_songs): user_songs_users = [] for i in range(0, len(user_songs)): user_songs_users.append(self.get_item_users(user_songs[i])) #设置矩阵大小为某一指定用户听过的所有歌曲×数据集中歌曲总数 cooccurence_matrix = np.matrix(np.zeros(shape=(len(user_songs), len(all_songs))), float) for i in range(0, len(all_songs)): #找出用户听过的第i首歌被哪些人听过 songs_i_data = self.train_data[self.train_data[self.item_id] == all_songs[i]] users_i = set(songs_i_data[self.user_id].unique()) #找出歌曲集中第j首歌被哪些人听过 for j in range(0, len(user_songs)): users_j = user_songs_users[j] # 计算听过i歌曲人数和j歌曲人数的交集 users_intersection = users_i.intersection(users_j) if len(users_intersection) != 0: # 计算听过i歌曲人数和j歌曲人数的并集 users_union = users_i.union(users_j) #使用Jaccard系数计算i,j之间的相似度 cooccurence_matrix[j,i] = float(len(users_intersection)) / float(len(users_union)) else: cooccurence_matrix[j,i] = 0 return cooccurence_matrix # 使用相似度矩阵进行topN推荐 def generate_top_recommendations(self, user, cooccurence_matrix, all_songs, user_songs): print("Non zero values in cooccurence_matrix :%d" % np.count_nonzero(cooccurence_matrix)) # 对每一首待推荐歌曲，计算其与用户听过的所有歌曲相似度的平均值 user_sim_scores = cooccurence_matrix.sum(axis=0) / float(cooccurence_matrix.shape[0]) user_sim_scores = np.array(user_sim_scores)[0].tolist() sort_index = sorted(((e, i) for i, e in enumerate(list(user_sim_scores))), reverse=True) columns = ['user_id', 'song', 'score', 'rank'] df = pandas.DataFrame(columns=columns) # 推荐相似度最高的5首歌 rank = 1 for i in range(0, len(sort_index)): if ~np.isnan(sort_index[i][0]) and all_songs[sort_index[i][1]] not in user_songs and rank <= 5: df.loc[len(df)] = [user, all_songs[sort_index[i][1]], sort_index[i][0], rank] rank = rank + 1 if df.shape[0] == 0: print("The current user has no songs for training the item similarity based recommendation model.") return -1 else: return df # 创建基于项目的协同过滤推荐模型 def create(self, train_data, user_id, item_id): self.train_data = train_data self.user_id = user_id self.item_id = item_id #进行推荐 def recommend(self, user): user_songs = self.get_user_items(user) print("No. of unique songs for the user: %d" % len(user_songs)) all_songs = self.get_all_items_train_data() print("no. of unique songs in the training set: %d" % len(all_songs)) cooccurence_matrix = self.construct_cooccurence_matrix(user_songs, all_songs) df_recommendations = self.generate_top_recommendations(user, cooccurence_matrix, all_songs, user_songs) return df_recommendations """ #构建相似度矩阵,考虑用户活跃度影响，进行矩阵归一化 def construct_cooccurence_matrix(self, user_songs, all_songs): # 每首歌都找出听过的人 user_songs_users = [] for i in range(0, len(user_songs)): user_songs_users.append(self.get_item_users(user_songs[i])) # 矩阵大小： 用户听过的歌曲数×歌曲总数 cooccurence_matrix = np.matrix(np.zeros(shape=(len(user_songs), len(all_songs))), float) # 计算用户歌曲和其他所有歌曲的相似度 for i in range(0, len(all_songs)): #从所有歌中确定了某首歌 songs_i_data = self.train_data[self.train_data[self.item_id] == all_songs[i]] #找出了听过歌的用户 users_i = set(songs_i_data[self.user_id].unique()) for j in range(0, len(user_songs)): #听过j的人 users_j = user_songs_users[j] #听过i和j的人的交集 users_intersection = users_i.intersection(users_j) if len(users_intersection) != 0: for k in users_intersection: user_k = self.get_user_items(k) cooccurence_matrix[j, i] += 1/math.log(1 + len(user_k)*1.0) cooccurence_matrix[j, i] = float(cooccurence_matrix[j, i]/math.sqrt(len(users_i)*len(users_j))) else: cooccurence_matrix[j, i] = 0 coo_max = cooccurence_matrix.max(axis=1) cooccurence_matrix = cooccurence_matrix/coo_max #print(cooccurence_matrix) return cooccurence_matrix """