def main_flow():
user_click = reader.get_user_click("../data/ratings.dat")
item_info = reader.get_item_info("../data/movies.dat")
sim_info = cal_item_sim(user_click)
recom_result = cal_recom_result(sim_info, user_click)
debug_recomresult(recom_result, item_info)
def main_flow():
"""
main flow of itemcf
"""
user_click, user_click_time = reader.get_user_click("../data/ratings.csv")
item_info = reader.get_item_info("../data/movies.csv")
sim_info = cal_item_sim(user_click, user_click_time)
debug_itemsim(item_info, sim_info)
def main_flow():
"""
main flow
"""
user_click,user_click_time = reader.get_user_click("../data/ratings.csv")
item_info = reader.get_item_info("../data/movies.csv")
item_click_by_user = transfer_user_click(user_click)
user_sim = cal_user_sim(item_click_by_user,user_click_time)
debug_user_sim(user_sim)
def main_flow():
"""
main flow of itemcf
:return:
"""
user_click = reader.get_user_click("../data/ratings.csv")
item_info = reader.get_item_info("../data/movies.csv")
sim_info = cal_item_sim(user_click)
# debug_itemsim(item_info,sim_info)
recom_result = cal_recom_result(sim_info, user_click)
debug_recomresult(recom_result, item_info)
def main_flow():
"""
main flow
"""
user_click, user_click_time = reader.get_user_click("../data/ratings.csv")
item_info = reader.get_item_info("../data/movies.csv")
item_click_by_user = transfer_user_click(user_click)
user_sim = cal_user_sim(item_click_by_user, user_click_time)
debug_user_sim(user_sim)
recom_result = cal_recom_result(user_click, user_sim)
# print(recom_result["1"])
debug_recom_result(item_info, recom_result)
def main_flow():
"""
main flow of item_cf
"""
user_click, user_click_time = get_user_click(
'../data/ratings.csv') # 获得user[item1,item2]
# 计算item与item直接的相似度
sim_info = cal_item_sim(user_click, user_click_time)
# print(sim_info)
# 计算推荐结果
recommend_result = cal_recommend_result(sim_info, user_click)
item_info = get_item_info(
'../data/movies.csv') # 获得{movieId:[title, genre]}
debug_item_sim(item_info, sim_info)
debug_recommend_result(recommend_result, item_info)
def main_flow():
"""
1,计算得到item的相似度
2，根据相似度进行推荐
3, 计算召回率和准确率
"""
# 主功能区
# step1:获得用户的点击序列 【获取到公式升级2的时间差】
user_click, user_click_time = reader.get_user_click(
"../data/split/rat_train.txt")
item_info = reader.get_item_info("../data/movies.txt")
# step2:根据用户的点击序列，得到Item的相似度
# sim_info = cal_item_sim(user_click)
# 【升级2】在计算item的相似度的时候需要用到user_click_time
sim_info = cal_item_sim(user_click, user_click_time)
# debug itemsim使用
debug_itemsim(item_info, sim_info)
# step3:根据用户的点击序列以及item的sim_info,去计算推荐结果
recom_result = cal_recom_result(sim_info, user_click)
# print(recom_result["1"])
# debug recomresult使用
debug_recomresult(recom_result, item_info)
# 计算评测指标(recom_result即是[user,itemid])
# 1.获取test的用户点击序列
test_userClick = reader.get_user_click("../data/split/rat_test.txt")[0]
# 2.进入函数Recall\Precision\Coverage\Populartity
recall = evaluation.Recall(recom_result, test_userClick)
precision = evaluation.Precision(recom_result, test_userClick)
coverage = evaluation.Coverage(recom_result, item_info)
populartity = evaluation.Populartity(recom_result)
print("recall（召回率）:" + str(recall) + "\t" + "precision（准确率）:" +
str(precision))
print("coverage（覆盖率）:" + str(coverage) + "\t" + "populartity（新颖度）:" +
str(populartity))
def main_flow():
"""
main_flow
1,计算用户的相似度矩阵
2，根据user相似度矩阵给user推荐物品
"""
# step1:获得用户的点击序列
user_click, user_click_time = reader.get_user_click(
"../data/split/rat_train.txt")
item_info = reader.get_item_info("../data/movies.txt")
# step2:同一个用户点击的item有哪些，根据此贡献值进行计算，所以需要将user_click转化成item_click_by_user，并计算其相似度
item_click_by_user = transfer_user_click(user_click)
# user_sim = cal_user_sim(item_click_by_user)
# 【公式升级2】中即需要考虑到用户对同一物品点击的时间差，时间差越小，贡献度越高
user_sim = cal_user_sim(item_click_by_user, user_click_time)
# debug相似度的函数
debug_user_sim(user_sim)
# step3:根据用户的相似度矩阵，及点击序列，即可得到对用户的推荐结果
recom_result = cal_recom_result(user_click, user_sim)
# debug推荐结果的函数
debug_recom_result(item_info, recom_result)
print(recom_result["1"])
# 计算评测指标(recom_result即是[user,itemid])
# 1.获取test的用户点击序列
test_userClick = reader.get_user_click("../data/split/rat_test.txt")[0]
# 2.进入函数Recall\Precision\Coverage\Populartity
recall = evaluation.Recall(recom_result, test_userClick)
precision = evaluation.Precision(recom_result, test_userClick)
coverage = evaluation.Coverage(recom_result, item_info)
populartity = evaluation.Populartity(recom_result)
print("recall（召回率）:" + str(recall) + "\t" + "precision（准确率）:" +
str(precision))
print("coverage（覆盖率）:" + str(coverage) + "\t" + "populartity（新颖度）:" +
str(populartity))
def main_flow():
user_click, user_click_time = reader.get_user_click('../data/ratings.csv')
item_info = reader.get_item_info('../data/movies.csv')
sim_info = cal_item_sim(user_click, user_click_time)
debug_itemsim(item_info, sim_info)
