通过SR-GNN算法进行挖掘商品图的时序商品推荐-数据集

import numpy as np import pandas as pd import scipy.sparse as sp def load_all(trainRatingPath, testNegativePath): """ We load all the three file here to save time in each epoch. """ train_data = pd.read_csv( trainRatingPath, sep='\t', header=None, names=['user', 'item'], usecols=[0, 1], dtype={0: np.int32, 1: np.int32}) user_num = train_data['user'].max() + 1 item_num = train_data['item'].max() + 1 train_data = train_data.values.tolist() # load ratings as a dok matrix train_mat = sp.dok_matrix((user_num, item_num), dtype=np.float32) for x in train_data: train_mat[x[0], x[1]] = 1.0 test_data = [] with open(testNegativePath, 'r') as fd: line = fd.readline() while line != None and line != '': arr = line.split('\t') u = eval(arr[0])[0] test_data.append([u, eval(arr[0])[1]]) for i in arr[1:-1]: test_data.append([u, int(i)]) line = fd.readline() return train_data, test_data, user_num, item_num, train_mat