def PrecisionRecall(train, test, W, N, K): # N为推荐物品的TopN，K为和用户兴趣相似的K个用户
hit = 0
p_all = 0
r_all = 0
for user in train.keys():
tu = test.get(user, {}) # 用户user在测试集上喜欢的物品集合，是一个字典
# if tu: # 由于|R(u)|=N是人为设定要推荐的物品数，所以此处就没必要为tu为空的user做推荐计算了
rank_all = ItemCF.ItemCF_IUFNormRecommend(train, user, W,
K) # K为指定的相似物品数
rank_TopN = sorted(rank_all.items(), key=itemgetter(1),
reverse=True)[0:N]
# 推荐给用户user的TopN物品及对应的预测出来的user对该物品的兴趣度
# for item, pui in rank_all.items():
for item, pui in rank_TopN:
if item in tu:
hit += 1
p_all += N
# p_all += len(rank_all)
r_all += len(tu)
return hit / p_all, hit / r_all
def Coverge(train, W, N, K):
recommend_items = set()
all_items = set()
for user in train.keys():
for item in train[user].keys():
all_items.add(item)
rank_all = ItemCF.ItemCF_IUFNormRecommend(train, user, W, K)
rank_TopN = sorted(rank_all.items(), key=itemgetter(1),
reverse=True)[0:N]
# for item, pui in rank_all.items():
for item, pui in rank_TopN:
recommend_items.add(item)
return len(recommend_items) / len(all_items)
def Novelty(train, W, N, K):
item_popularity = PopularityNums(train)
ret = 0
n = 0 # n=用户数*TopN推荐列表中物品数N（推荐的物品总数）
for user in train.keys():
rank_all = ItemCF.ItemCF_IUFNormRecommend(train, user, W, K)
rank_TopN = sorted(rank_all.items(), key=itemgetter(1),
reverse=True)[0:N]
# for item, pui in rank_all.items():
for item, pui in rank_TopN:
ret += math.log(1 + item_popularity[item])
n += 1
return ret / n
