一、数据背景经过7年的发展与沉淀目前阿里音乐拥有数百万的曲库资源每天千万的用户活跃在平台上拥有数亿人次的用户试听、收藏等行为。在原创艺人和作品方面更是拥有数万的独立音乐人每月上传上万个原创作品形成超过几十万首曲目的原创作品库如此庞大的数据资源库对于音乐流行趋势的把握有着极为重要的指引作用。本次大赛以阿里音乐用户的历史播放数据为基础期望研究人员可以通过对阿里音乐平台上每个阶段艺人的试听量的预测挖掘出即将成为潮流的艺人从而实现对一个时间段内音乐流行趋势的准确把控。数据来源音乐流行趋势预测数据集_数据集-阿里云天池二、模型介绍矩阵分解算法矩阵分解的核心思想是将用户和项目映射到同一个低维隐空间中通过隐向量的内积来刻画用户对项目的潜在偏好。这种隐语义模型假设用户对艺人的喜爱程度并非由某个显式特征直接决定而是受到若干隐含因子的共同影响例如音乐风格、演唱方式、情感表达等。传统协同过滤算法基准模型基于用户的协同过滤UserCF是最早被广泛应用的推荐算法之一其基本假设是具有相似试听历史的用户会对未来听到的艺人表现出相似的偏好。具体地对于目标用户u算法首先计算用户u与其他所有用户在用户-艺人交互矩阵上的相似度常用的相似度度量有余弦相似度和皮尔逊相关系数。评估函数本次实验用了RMSE、MAE。三、实验过程1.前期导入库import warnings import time import pandas as pd import numpy as np import matplotlib.pyplot as plt from scipy.spatial.distance import cosine from sklearn.preprocessing import MinMaxScaler from sklearn.metrics import mean_squared_error, mean_absolute_error warnings.filterwarnings(ignore) plt.rcParams[font.family] [SimHei] plt.rcParams[axes.unicode_minus] False DATA_PATH r # 请修改为你的实际路径 CF_K 30 PMF_D_LIST [10, 20, 30] TRAIN_MONTH 6 TEST_MONTH 1 NDCG_K1 10 NDCG_K2 20 RANDOM_SEED 42 np.random.seed(RANDOM_SEED)建议用镜像源安装。2.数据清洗# 读取无表头的Excel手动赋予列名 df pd.read_excel(DATA_PATH, headerNone) df.columns [user_id, song_id, gmt_create, action_type, Ds] print(f原始行数: {len(df)}) print(前3行原始数据) print(df.head(3)) # 重命名 song_id - artist_id df.rename(columns{song_id: artist_id}, inplaceTrue) print(\naction_type 原始类型:, df[action_type].dtype) print(action_type 唯一值:, df[action_type].unique()[:10]) # 强制转换为整数如果是字符串则转换 df[action_type] pd.to_numeric(df[action_type], errorscoerce) print(转换后 action_type 类型:, df[action_type].dtype) print(转换后唯一值:, df[action_type].unique()[:10]) # 只保留播放行为action_type 1 df df[df[action_type] 1].copy() print(f过滤播放行为后行数: {len(df)}) if len(df) 0: raise Exception(错误没有播放行为数据请检查 action_type 列的值) # 添加播放次数 df[play_count] 1 # 时间转换 df[action_time] pd.to_datetime(df[gmt_create], units) invalid_time df[action_time].isna().sum() if invalid_time 0: print(f警告有 {invalid_time} 行时间戳无效将被丢弃) df df.dropna(subset[action_time]) print(f有效时间数据行数: {len(df)}) # 提取年月 df[year_month] df[action_time].dt.to_period(M) print(f时间范围: {df[year_month].min()} 至 {df[year_month].max()}) print(各月份数据量) print(df[year_month].value_counts().sort_index())这里要注意确保 action_type 是整数类型3.过滤低频交互user_counts df.groupby(user_id)[play_count].sum() artist_counts df.groupby(artist_id)[play_count].sum() min_user_plays 5 min_artist_plays 5 keep_users user_counts[user_counts min_user_plays].index keep_artists artist_counts[artist_counts min_artist_plays].index before len(df) df df[df[user_id].isin(keep_users) df[artist_id].isin(keep_artists)] print(f过滤后剩余行数: {len(df)} (原{before}行)) print(f剩余用户数: {df[user_id].nunique()}) print(f剩余物品数: {df[artist_id].nunique()})4.时间窗口划分print(\n 板块4时间窗口划分 ) df df.sort_values(action_time).reset_index(dropTrue) all_months sorted(df[year_month].unique()) print(数据中的所有月份:, all_months) print(f总月份数: {len(all_months)}) if len(all_months) TRAIN_MONTH TEST_MONTH: print(f警告总月份数 {len(all_months)} 不足 {TRAIN_MONTHTEST_MONTH}将调整训练集月份数) if len(all_months) TEST_MONTH: raise Exception(错误数据月份数不足无法分出测试集请检查时间转换或使用更多数据。) TRAIN_MONTH len(all_months) - TEST_MONTH print(f调整后训练月份数: {TRAIN_MONTH}) train_months all_months[:TRAIN_MONTH] test_months all_months[TRAIN_MONTH:TRAIN_MONTHTEST_MONTH] df_train df[df[year_month].isin(train_months)] df_test df[df[year_month].isin(test_months)] print(f训练集月份: {train_months}, 样本数: {len(df_train)}) print(f测试集月份: {test_months}, 样本数: {len(df_test)})5.构建交互矩阵def build_interaction_matrix(df, user2idxNone, artist2idxNone): if user2idx is None: users df[user_id].unique() artists df[artist_id].unique() user2idx {u: i for i, u in enumerate(users)} artist2idx {a: i for i, a in enumerate(artists)} R np.zeros((len(users), len(artists)), dtypenp.float32) else: R np.zeros((len(user2idx), len(artist2idx)), dtypenp.float32) grouped df.groupby([user_id, artist_id])[play_count].sum() for (u, a), val in grouped.items(): if u in user2idx and a in artist2idx: R[user2idx[u], artist2idx[a]] val return R, user2idx, artist2idx print(\n 板块5构建训练矩阵 ) R_train_raw, user2idx, artist2idx build_interaction_matrix(df_train) print(f训练矩阵形状: {R_train_raw.shape}) print(f非零元素数: {np.count_nonzero(R_train_raw)}) # 对数变换 归一化 R_log np.log1p(R_train_raw) scaler MinMaxScaler() R_train scaler.fit_transform(R_log) sparsity (1 - np.count_nonzero(R_train) / R_train.size) * 100 print(f训练矩阵稀疏度: {sparsity:.2f}%) print(f矩阵内存占用: {R_train.nbytes / 1024**2:.2f} MB) print(\n构建测试矩阵使用训练集的索引) R_test_raw, _, _ build_interaction_matrix(df_test, user2idx, artist2idx) R_test_log np.log1p(R_test_raw) R_test scaler.transform(R_test_log) print(f测试矩阵形状: {R_test.shape}) print(f测试集非零元素数: {np.count_nonzero(R_test_raw)})6.评估指标函数def calc_rmse(y_true, y_pred): return np.sqrt(mean_squared_error(y_true, y_pred)) def calc_mae(y_true, y_pred): return mean_absolute_error(y_true, y_pred) def calc_ndcg(rank_list, k): if len(rank_list) 0: return 0.0 rank_list np.array(rank_list)[:k] dcg np.sum(rank_list / np.log2(np.arange(2, len(rank_list)2))) idcg np.sum(np.sort(rank_list)[::-1] / np.log2(np.arange(2, len(rank_list)2))) return dcg / idcg if idcg ! 0 else 0.0 def evaluate_model(pred_mat, true_mat): mask true_mat 0 if not np.any(mask): return {RMSE: 0.0, MAE: 0.0, NDCG10: 0.0, NDCG20: 0.0} y_true true_mat[mask] y_pred pred_mat[mask] rmse calc_rmse(y_true, y_pred) mae calc_mae(y_true, y_pred) ndcg10_list [] ndcg20_list [] n_items true_mat.shape[1] for a in range(n_items): pred_rank pred_mat[:, a] ndcg10_list.append(calc_ndcg(pred_rank, NDCG_K1)) ndcg20_list.append(calc_ndcg(pred_rank, NDCG_K2)) return { RMSE: rmse, MAE: mae, NDCG10: np.mean(ndcg10_list), NDCG20: np.mean(ndcg20_list) }7协同过滤模型class UserCF: def __init__(self, R, k): self.R R self.k k self.n_user R.shape[0] self.sim self._calc_user_similarity() def _calc_user_similarity(self): sim np.zeros((self.n_user, self.n_user)) for i in range(self.n_user): for j in range(i1, self.n_user): s 1 - cosine(self.R[i], self.R[j]) sim[i, j] sim[j, i] s return sim def predict(self): pred np.zeros_like(self.R) for u in range(self.n_user): neighbors np.argsort(self.sim[u])[::-1][1:self.k1] if len(neighbors) 0: pred[u] np.mean(self.R[neighbors], axis0) return pred class ItemCF: def __init__(self, R, k): self.R R self.k k self.n_item R.shape[1] self.sim self._calc_item_similarity() def _calc_item_similarity(self): sim np.zeros((self.n_item, self.n_item)) for i in range(self.n_item): for j in range(i1, self.n_item): s 1 - cosine(self.R[:, i], self.R[:, j]) sim[i, j] sim[j, i] s return sim def predict(self): pred np.zeros_like(self.R) for a in range(self.n_item): neighbors np.argsort(self.sim[a])[::-1][1:self.k1] if len(neighbors) 0: pred[:, a] np.mean(self.R[:, neighbors], axis1) return pred8概率矩阵分解 PMFclass PMF: def __init__(self, R, latent_dim, lr0.01, reg0.01, epochs30): self.R R self.n_user, self.n_item R.shape self.d latent_dim self.lr lr self.reg reg self.epochs epochs self.U np.random.normal(0, 0.1, (self.n_user, self.d)) self.V np.random.normal(0, 0.1, (self.n_item, self.d)) def train(self, verboseTrue): for epoch in range(self.epochs): pred self.U self.V.T grad_U -2 * (self.R - pred) self.V 2 * self.reg * self.U grad_V -2 * (self.R - pred).T self.U 2 * self.reg * self.V self.U - self.lr * grad_U self.V - self.lr * grad_V if verbose and (epoch1) % 10 0: loss np.sum((self.R - pred)**2) self.reg*(np.sum(self.U**2)np.sum(self.V**2)) print(f PMF d{self.d}, epoch {epoch1}/{self.epochs}, loss{loss:.2f}) def predict(self): return self.U self.V.T9训练 UserCF、ItemCF 和 PMF 网格搜索t0 time.time() usercf UserCF(R_train, CF_K) pred_usercf usercf.predict() time_usercf time.time() - t0 res_usercf evaluate_model(pred_usercf, R_test) print(fUserCF 耗时 {time_usercf:.2f}s, RMSE{res_usercf[RMSE]:.4f}) print(\n 训练 ItemCF ) t0 time.time() itemcf ItemCF(R_train, CF_K) pred_itemcf itemcf.predict() time_itemcf time.time() - t0 res_itemcf evaluate_model(pred_itemcf, R_test) print(fItemCF 耗时 {time_itemcf:.2f}s, RMSE{res_itemcf[RMSE]:.4f}) print(\n 训练 PMF 并搜索最佳维度 ) best_res None best_pred None best_d None pmf_results {} for d in PMF_D_LIST: t0 time.time() pmf PMF(R_train, d, epochs30) pmf.train() pred pmf.predict() cost time.time() - t0 res evaluate_model(pred, R_test) pmf_results[d] (res, cost, pred) print(fd{d} 完成, RMSE{res[RMSE]:.4f}, 耗时{cost:.2f}s) if best_res is None or res[RMSE] best_res[RMSE]: best_res res best_pred pred best_d d10结果result_df pd.DataFrame({ 模型: [UserCF, ItemCF, fPMF(d{best_d})], RMSE: [res_usercf[RMSE], res_itemcf[RMSE], best_res[RMSE]], MAE: [res_usercf[MAE], res_itemcf[MAE], best_res[MAE]], NDCG10: [res_usercf[NDCG10], res_itemcf[NDCG10], best_res[NDCG10]], NDCG20: [res_usercf[NDCG20], res_itemcf[NDCG20], best_res[NDCG20]], 耗时(s): [time_usercf, time_itemcf, pmf_results[best_d][1]] }) print(result_df.round(4))11可视化# PMF 参数影响图 d_vals list(pmf_results.keys()) rmse_vals [pmf_results[d][0][RMSE] for d in d_vals] ndcg_vals [pmf_results[d][0][NDCG10] for d in d_vals] fig, axes plt.subplots(1, 2, figsize(12, 4)) axes[0].plot(d_vals, rmse_vals, o-, colorred) axes[0].set_xlabel(Latent Dimension d) axes[0].set_ylabel(RMSE) axes[0].set_title(PMF RMSE vs d) axes[0].grid(True) axes[1].plot(d_vals, ndcg_vals, s-, colorblue) axes[1].set_xlabel(Latent Dimension d) axes[1].set_ylabel(NDCG10) axes[1].set_title(PMF NDCG10 vs d) axes[1].grid(True) plt.tight_layout() plt.show() # 模型对比柱状图 models [UserCF, ItemCF, fPMF(d{best_d})] rmse_all [res_usercf[RMSE], res_itemcf[RMSE], best_res[RMSE]] ndcg_all [res_usercf[NDCG10], res_itemcf[NDCG10], best_res[NDCG10]] fig, axes plt.subplots(1, 2, figsize(12, 4)) axes[0].bar(models, rmse_all, color[#1f77b4, #ff7f0e, #2ca02c]) axes[0].set_ylabel(RMSE (lower better)) axes[0].set_title(模型 RMSE 对比) axes[1].bar(models, ndcg_all, color[#1f77b4, #ff7f0e, #2ca02c]) axes[1].set_ylabel(NDCG10 (higher better)) axes[1].set_title(模型 NDCG10 对比) plt.tight_layout() plt.show() 机器学习-音乐艺人流行趋势预测
一、数据背景经过7年的发展与沉淀目前阿里音乐拥有数百万的曲库资源每天千万的用户活跃在平台上拥有数亿人次的用户试听、收藏等行为。在原创艺人和作品方面更是拥有数万的独立音乐人每月上传上万个原创作品形成超过几十万首曲目的原创作品库如此庞大的数据资源库对于音乐流行趋势的把握有着极为重要的指引作用。本次大赛以阿里音乐用户的历史播放数据为基础期望研究人员可以通过对阿里音乐平台上每个阶段艺人的试听量的预测挖掘出即将成为潮流的艺人从而实现对一个时间段内音乐流行趋势的准确把控。数据来源音乐流行趋势预测数据集_数据集-阿里云天池二、模型介绍矩阵分解算法矩阵分解的核心思想是将用户和项目映射到同一个低维隐空间中通过隐向量的内积来刻画用户对项目的潜在偏好。这种隐语义模型假设用户对艺人的喜爱程度并非由某个显式特征直接决定而是受到若干隐含因子的共同影响例如音乐风格、演唱方式、情感表达等。传统协同过滤算法基准模型基于用户的协同过滤UserCF是最早被广泛应用的推荐算法之一其基本假设是具有相似试听历史的用户会对未来听到的艺人表现出相似的偏好。具体地对于目标用户u算法首先计算用户u与其他所有用户在用户-艺人交互矩阵上的相似度常用的相似度度量有余弦相似度和皮尔逊相关系数。评估函数本次实验用了RMSE、MAE。三、实验过程1.前期导入库import warnings import time import pandas as pd import numpy as np import matplotlib.pyplot as plt from scipy.spatial.distance import cosine from sklearn.preprocessing import MinMaxScaler from sklearn.metrics import mean_squared_error, mean_absolute_error warnings.filterwarnings(ignore) plt.rcParams[font.family] [SimHei] plt.rcParams[axes.unicode_minus] False DATA_PATH r # 请修改为你的实际路径 CF_K 30 PMF_D_LIST [10, 20, 30] TRAIN_MONTH 6 TEST_MONTH 1 NDCG_K1 10 NDCG_K2 20 RANDOM_SEED 42 np.random.seed(RANDOM_SEED)建议用镜像源安装。2.数据清洗# 读取无表头的Excel手动赋予列名 df pd.read_excel(DATA_PATH, headerNone) df.columns [user_id, song_id, gmt_create, action_type, Ds] print(f原始行数: {len(df)}) print(前3行原始数据) print(df.head(3)) # 重命名 song_id - artist_id df.rename(columns{song_id: artist_id}, inplaceTrue) print(\naction_type 原始类型:, df[action_type].dtype) print(action_type 唯一值:, df[action_type].unique()[:10]) # 强制转换为整数如果是字符串则转换 df[action_type] pd.to_numeric(df[action_type], errorscoerce) print(转换后 action_type 类型:, df[action_type].dtype) print(转换后唯一值:, df[action_type].unique()[:10]) # 只保留播放行为action_type 1 df df[df[action_type] 1].copy() print(f过滤播放行为后行数: {len(df)}) if len(df) 0: raise Exception(错误没有播放行为数据请检查 action_type 列的值) # 添加播放次数 df[play_count] 1 # 时间转换 df[action_time] pd.to_datetime(df[gmt_create], units) invalid_time df[action_time].isna().sum() if invalid_time 0: print(f警告有 {invalid_time} 行时间戳无效将被丢弃) df df.dropna(subset[action_time]) print(f有效时间数据行数: {len(df)}) # 提取年月 df[year_month] df[action_time].dt.to_period(M) print(f时间范围: {df[year_month].min()} 至 {df[year_month].max()}) print(各月份数据量) print(df[year_month].value_counts().sort_index())这里要注意确保 action_type 是整数类型3.过滤低频交互user_counts df.groupby(user_id)[play_count].sum() artist_counts df.groupby(artist_id)[play_count].sum() min_user_plays 5 min_artist_plays 5 keep_users user_counts[user_counts min_user_plays].index keep_artists artist_counts[artist_counts min_artist_plays].index before len(df) df df[df[user_id].isin(keep_users) df[artist_id].isin(keep_artists)] print(f过滤后剩余行数: {len(df)} (原{before}行)) print(f剩余用户数: {df[user_id].nunique()}) print(f剩余物品数: {df[artist_id].nunique()})4.时间窗口划分print(\n 板块4时间窗口划分 ) df df.sort_values(action_time).reset_index(dropTrue) all_months sorted(df[year_month].unique()) print(数据中的所有月份:, all_months) print(f总月份数: {len(all_months)}) if len(all_months) TRAIN_MONTH TEST_MONTH: print(f警告总月份数 {len(all_months)} 不足 {TRAIN_MONTHTEST_MONTH}将调整训练集月份数) if len(all_months) TEST_MONTH: raise Exception(错误数据月份数不足无法分出测试集请检查时间转换或使用更多数据。) TRAIN_MONTH len(all_months) - TEST_MONTH print(f调整后训练月份数: {TRAIN_MONTH}) train_months all_months[:TRAIN_MONTH] test_months all_months[TRAIN_MONTH:TRAIN_MONTHTEST_MONTH] df_train df[df[year_month].isin(train_months)] df_test df[df[year_month].isin(test_months)] print(f训练集月份: {train_months}, 样本数: {len(df_train)}) print(f测试集月份: {test_months}, 样本数: {len(df_test)})5.构建交互矩阵def build_interaction_matrix(df, user2idxNone, artist2idxNone): if user2idx is None: users df[user_id].unique() artists df[artist_id].unique() user2idx {u: i for i, u in enumerate(users)} artist2idx {a: i for i, a in enumerate(artists)} R np.zeros((len(users), len(artists)), dtypenp.float32) else: R np.zeros((len(user2idx), len(artist2idx)), dtypenp.float32) grouped df.groupby([user_id, artist_id])[play_count].sum() for (u, a), val in grouped.items(): if u in user2idx and a in artist2idx: R[user2idx[u], artist2idx[a]] val return R, user2idx, artist2idx print(\n 板块5构建训练矩阵 ) R_train_raw, user2idx, artist2idx build_interaction_matrix(df_train) print(f训练矩阵形状: {R_train_raw.shape}) print(f非零元素数: {np.count_nonzero(R_train_raw)}) # 对数变换 归一化 R_log np.log1p(R_train_raw) scaler MinMaxScaler() R_train scaler.fit_transform(R_log) sparsity (1 - np.count_nonzero(R_train) / R_train.size) * 100 print(f训练矩阵稀疏度: {sparsity:.2f}%) print(f矩阵内存占用: {R_train.nbytes / 1024**2:.2f} MB) print(\n构建测试矩阵使用训练集的索引) R_test_raw, _, _ build_interaction_matrix(df_test, user2idx, artist2idx) R_test_log np.log1p(R_test_raw) R_test scaler.transform(R_test_log) print(f测试矩阵形状: {R_test.shape}) print(f测试集非零元素数: {np.count_nonzero(R_test_raw)})6.评估指标函数def calc_rmse(y_true, y_pred): return np.sqrt(mean_squared_error(y_true, y_pred)) def calc_mae(y_true, y_pred): return mean_absolute_error(y_true, y_pred) def calc_ndcg(rank_list, k): if len(rank_list) 0: return 0.0 rank_list np.array(rank_list)[:k] dcg np.sum(rank_list / np.log2(np.arange(2, len(rank_list)2))) idcg np.sum(np.sort(rank_list)[::-1] / np.log2(np.arange(2, len(rank_list)2))) return dcg / idcg if idcg ! 0 else 0.0 def evaluate_model(pred_mat, true_mat): mask true_mat 0 if not np.any(mask): return {RMSE: 0.0, MAE: 0.0, NDCG10: 0.0, NDCG20: 0.0} y_true true_mat[mask] y_pred pred_mat[mask] rmse calc_rmse(y_true, y_pred) mae calc_mae(y_true, y_pred) ndcg10_list [] ndcg20_list [] n_items true_mat.shape[1] for a in range(n_items): pred_rank pred_mat[:, a] ndcg10_list.append(calc_ndcg(pred_rank, NDCG_K1)) ndcg20_list.append(calc_ndcg(pred_rank, NDCG_K2)) return { RMSE: rmse, MAE: mae, NDCG10: np.mean(ndcg10_list), NDCG20: np.mean(ndcg20_list) }7协同过滤模型class UserCF: def __init__(self, R, k): self.R R self.k k self.n_user R.shape[0] self.sim self._calc_user_similarity() def _calc_user_similarity(self): sim np.zeros((self.n_user, self.n_user)) for i in range(self.n_user): for j in range(i1, self.n_user): s 1 - cosine(self.R[i], self.R[j]) sim[i, j] sim[j, i] s return sim def predict(self): pred np.zeros_like(self.R) for u in range(self.n_user): neighbors np.argsort(self.sim[u])[::-1][1:self.k1] if len(neighbors) 0: pred[u] np.mean(self.R[neighbors], axis0) return pred class ItemCF: def __init__(self, R, k): self.R R self.k k self.n_item R.shape[1] self.sim self._calc_item_similarity() def _calc_item_similarity(self): sim np.zeros((self.n_item, self.n_item)) for i in range(self.n_item): for j in range(i1, self.n_item): s 1 - cosine(self.R[:, i], self.R[:, j]) sim[i, j] sim[j, i] s return sim def predict(self): pred np.zeros_like(self.R) for a in range(self.n_item): neighbors np.argsort(self.sim[a])[::-1][1:self.k1] if len(neighbors) 0: pred[:, a] np.mean(self.R[:, neighbors], axis1) return pred8概率矩阵分解 PMFclass PMF: def __init__(self, R, latent_dim, lr0.01, reg0.01, epochs30): self.R R self.n_user, self.n_item R.shape self.d latent_dim self.lr lr self.reg reg self.epochs epochs self.U np.random.normal(0, 0.1, (self.n_user, self.d)) self.V np.random.normal(0, 0.1, (self.n_item, self.d)) def train(self, verboseTrue): for epoch in range(self.epochs): pred self.U self.V.T grad_U -2 * (self.R - pred) self.V 2 * self.reg * self.U grad_V -2 * (self.R - pred).T self.U 2 * self.reg * self.V self.U - self.lr * grad_U self.V - self.lr * grad_V if verbose and (epoch1) % 10 0: loss np.sum((self.R - pred)**2) self.reg*(np.sum(self.U**2)np.sum(self.V**2)) print(f PMF d{self.d}, epoch {epoch1}/{self.epochs}, loss{loss:.2f}) def predict(self): return self.U self.V.T9训练 UserCF、ItemCF 和 PMF 网格搜索t0 time.time() usercf UserCF(R_train, CF_K) pred_usercf usercf.predict() time_usercf time.time() - t0 res_usercf evaluate_model(pred_usercf, R_test) print(fUserCF 耗时 {time_usercf:.2f}s, RMSE{res_usercf[RMSE]:.4f}) print(\n 训练 ItemCF ) t0 time.time() itemcf ItemCF(R_train, CF_K) pred_itemcf itemcf.predict() time_itemcf time.time() - t0 res_itemcf evaluate_model(pred_itemcf, R_test) print(fItemCF 耗时 {time_itemcf:.2f}s, RMSE{res_itemcf[RMSE]:.4f}) print(\n 训练 PMF 并搜索最佳维度 ) best_res None best_pred None best_d None pmf_results {} for d in PMF_D_LIST: t0 time.time() pmf PMF(R_train, d, epochs30) pmf.train() pred pmf.predict() cost time.time() - t0 res evaluate_model(pred, R_test) pmf_results[d] (res, cost, pred) print(fd{d} 完成, RMSE{res[RMSE]:.4f}, 耗时{cost:.2f}s) if best_res is None or res[RMSE] best_res[RMSE]: best_res res best_pred pred best_d d10结果result_df pd.DataFrame({ 模型: [UserCF, ItemCF, fPMF(d{best_d})], RMSE: [res_usercf[RMSE], res_itemcf[RMSE], best_res[RMSE]], MAE: [res_usercf[MAE], res_itemcf[MAE], best_res[MAE]], NDCG10: [res_usercf[NDCG10], res_itemcf[NDCG10], best_res[NDCG10]], NDCG20: [res_usercf[NDCG20], res_itemcf[NDCG20], best_res[NDCG20]], 耗时(s): [time_usercf, time_itemcf, pmf_results[best_d][1]] }) print(result_df.round(4))11可视化# PMF 参数影响图 d_vals list(pmf_results.keys()) rmse_vals [pmf_results[d][0][RMSE] for d in d_vals] ndcg_vals [pmf_results[d][0][NDCG10] for d in d_vals] fig, axes plt.subplots(1, 2, figsize(12, 4)) axes[0].plot(d_vals, rmse_vals, o-, colorred) axes[0].set_xlabel(Latent Dimension d) axes[0].set_ylabel(RMSE) axes[0].set_title(PMF RMSE vs d) axes[0].grid(True) axes[1].plot(d_vals, ndcg_vals, s-, colorblue) axes[1].set_xlabel(Latent Dimension d) axes[1].set_ylabel(NDCG10) axes[1].set_title(PMF NDCG10 vs d) axes[1].grid(True) plt.tight_layout() plt.show() # 模型对比柱状图 models [UserCF, ItemCF, fPMF(d{best_d})] rmse_all [res_usercf[RMSE], res_itemcf[RMSE], best_res[RMSE]] ndcg_all [res_usercf[NDCG10], res_itemcf[NDCG10], best_res[NDCG10]] fig, axes plt.subplots(1, 2, figsize(12, 4)) axes[0].bar(models, rmse_all, color[#1f77b4, #ff7f0e, #2ca02c]) axes[0].set_ylabel(RMSE (lower better)) axes[0].set_title(模型 RMSE 对比) axes[1].bar(models, ndcg_all, color[#1f77b4, #ff7f0e, #2ca02c]) axes[1].set_ylabel(NDCG10 (higher better)) axes[1].set_title(模型 NDCG10 对比) plt.tight_layout() plt.show()