def model_train_process():
train_data = read.get_train_data("../data/ratings.txt")
user_vec, item_vec = lfm_train(train_data, 50, 0.01, 0.1, 50)
recom_result = give_recom_result(user_vec, item_vec, '24')
ana_recom_result(train_data, '24', recom_result)
def model_train_process():
train_data = read.get_train_data("../data/ratings.txt")
user_vector, item_vector = lfm_train(train_data, 50, 0.01, 0.1, 50)
# print (user_vector[20])
# print (item_vector['157'])
for userid in user_vector:
recom_result = (get_recomend_result(user_vector, item_vector, userid))
def model_train_process():
"""test lfm model train"""
userId = '24'
train_data = read.get_train_data("../data/ratings.csv")
user_vec, item_vec = lfm_train(train_data, 10, 0.02, 0.2, 20)
recom_result = get_recom_result(user_vec, item_vec, userId)
analysis_recom_result(train_data, userId, recom_result)
def model_train_process():
"""
test lfm model train
:return:
"""
train_data = read.get_train_data("../data/ratings.txt")
user_vec, item_vec = lfm_train(train_data, 50, 0.01, 0.1, 50)
def model_train_process():
"""
test lfm model train
"""
train_data=read.get_train_data("../data/ratings.txt")
user_vec, item_vec = lfm_train(train_data, 50, 0.01, 0.1, 50)
for userid in user_vec:
recom_result = give_recom_result(user_vec, item_vec, userid)
def model_train_process():
train_data = read.get_train_data(read.get_rating_data())
user_vec, article_vec = lfm_train(train_data, 50, 0.01, 0.1, 50)
# print(user_vec["1"])
# recom_result = give_recom_result(user_vec, article_vec, '24')
# print(recom_result)
for user_id in user_vec:
threading.Thread(target=give_recom_result, args=(user_vec, article_vec, user_id)).start()
def model_train_process():
"""
test lfm model train
:return:
"""
train_data = read.get_train_data("../data/ratings.csv")
user_vec, item_vec = lfm_train(train_data, 50, 0.01, 0.1, 50)
recom_result = give_recom_result(user_vec, item_vec, '24')
ana_recom_result(train_data, '24', recom_result)
def model_train_process():
train_data = read.get_train_data(
os.path.dirname(
os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) +
"\\recommender\\data\\ml-latest-small\\ratings.csv")
user_vec, item_vec = lfm_train(train_data, 50, 0.01, 0.1, 50)
recom_list = give_recommender(user_vec, item_vec, '24')
ana_recom_result(train_data, "24", recom_list)
def model_train_process():
"""
test lfm model train
"""
train_data = read.get_train_data("../data/ratings.dat")
user_vec, item_vec = lfm_train(train_data, 50, 0.01, 0.1, 50)
recom_result = give_recom_result(user_vec, item_vec, '10')
# print(user_vec["1"])
# print(item_vec["2762"])
# print(give_recom_result(user_vec, item_vec, '10'))
ana_recom_result(train_data, '10', recom_result)
def model_train_process():
# 训练数据
train_data = get_train_data("../data/ml-latest-small/ratings.csv")
user_vec,item_vec = lfm(train_data, F = 50 , alpha = 0.01, beta = 0.1, step = 50)
print('user_vec:',len(user_vec),user_vec['1'])
print('item_vec:',len(item_vec),item_vec['1'])
recom_list = get_recom_result(user_vec, item_vec, userid = '24')
# 算法预测效果和用户真实评分记录对比
ana_recom_result(train_data, '24', recom_list)
def model_train_process():
"""
test lfm model train
:return:
"""
train_data = read.get_train_data("../data/myratings.txt")
user_vec, item_vec = lfm_train(train_data, 50, 0.01, 0.1, 50)
recom_result = give_recom_result(user_vec, item_vec, "1")
print(recom_result)
ana_recom_result(train_data, "1", recom_result)
def model_train_process():
"""
test lfm model train
"""
train_data = read.get_train_data("../data/ratings.dat")
user_vec, item_vec = lfm_train(train_data,
F=50,
alpha=0.01,
beta=0.1,
step=50)
print(user_vec["1"])
print(item_vec["2455"])
def model_train_process():
"""
test lfm model train
:return:
"""
# train_data = read.get_train_data("../data/ml-1m/ratings.txt")
train_data = read.get_train_data("../data/ratings.txt")
user_vec, item_vec = lfm_train(train_data, 50, 0.01, 0.1, 50)
recom_list = give_recom_result(user_vec, item_vec, '4')
print(recom_list)
ana_recom_result(train_data, '4', recom_list)
return user_vec, item_vec
def model_train_process():
"""
test lfm model train
"""
train_data = read.get_train_data("../data/split/rat_train.txt")
user_vec, item_vec = lfm_train(train_data, 50, 0.01, 0.1, 50)
# print(user_vec["1"])
# print(item_vec["50"])
# 基于一个用户的推荐
# recom_result = give_recom_result(user_vec,item_vec,'1')
# ana_recom_result(train_data,'1',recom_result)
# 基于所有用户的推荐
for userid in user_vec:
recom_result = give_recom_result(user_vec, item_vec, userid)
# 在此处定义一个数据结构(字典)中存储【userid,[itemid1,itemid2]】
ana_recom_result(train_data, userid, recom_result)
def model_train_process():
train_data=read.get_train_data('../data/ratings.txt')
user_vec,item_vec=lfm_train(train_data,50,0.01,0.1,50)
for userid in user_vec:
recom_result=give_recom_result(user_vec,item_vec,userid)
ana_recom_result(train_data,userid,recom_result)
