def run_main():
ave_score = read.get_ave_score("./data/ratings.txt")
item_cate, cate_item_sort = read.get_item_cate(ave_score, "./data/movies.txt")
up = get_up(item_cate, "./data/ratings.txt")
resutl = recom(cate_item_sort, up, "1")
print(resutl)
print(up["1"])
def run_main():
ave_score = read.get_ave_score('../data/ratings.txt')
item_cate, cate_item_sort = read.get_item_cate(ave_score,
'../data/movies.txt')
up = get_up(item_cate, '../data/ratings.txt')
#print(up['1'])
print(recom(cate_item_sort, up, '1'))
def run_main():
ave_score = read.get_ave_score("../data/ratings.csv")
item_cate, cate_item_sort = read.get_item_cate(ave_score,
"../data/movies.csv")
up = get_up(item_cate, "../data/ratings.csv")
print(len(up))
print(up["1"])
print(recom(cate_item_sort, up, "1"))
def run_main():
ave_score = read.get_ave_score("../data/ratings.csv")
item_cate, cate_item_sort = read.get_item_cate(ave_score,
"../data/movies.csv")
up = get_up(item_cate, "../data/ratings.csv")
# 少了442这个user，因为该用户的所有评分都小于4.0，说明该用户看过的所有电影都不感兴趣，很难进行推荐
print(len(up))
print(up["1"])
print(recom(cate_item_sort, up, "1"))
def run_main():
ave_score = read.get_ave_score("../data/ratings.dat")
item_cate, cate_sorted = read.get_item_cate(ave_score,
"../data/movies.dat")
up = get_up(item_cate, "../data/ratings.dat")
print(up["1"])
print(recom(
cate_sorted,
up,
"1",
))
def run_main():
ave_score = read.get_ave_score("../data/rating.txt")
item_cate, cate_item_sort = read.get_item_cate(ave_score, "../data/movies.txt")
up = get_up(item_cate, "../data/rating.txt")
print(len(up))
print(up["1"])
recom_result = {}
if userid not in recom_result:
recom_result[userid] = []
for comb in up[userid]:
category, ratio = comb
num = int(topK * ratio) + 1
if category not in cate_item_sort:
continue
recom_list = cate_item_sort[category][:num]
recom_result[userid] += recom_list
return recom_result
if __name__ == "__main__":
score_dict = read.get_ave_score(
os.path.dirname(
os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) +
"\\recommender\\data\\ml-latest-small\\ratings.csv")
item_cate, cate_item_scort = read.get_item_category(
score_dict,
os.path.dirname(
os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) +
"\\recommender\\data\\ml-latest-small\\movies.csv")
userProfile = get_up(
item_cate,
os.path.dirname(
os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) +
"\\recommender\\data\\ml-latest-small\\ratings.csv")
print(len(userProfile))
print(userProfile["1"])
recom_resilt = recom(cate_item_scort, userProfile, "1")
