def compute_cfus():
'''
计算给用户推荐的店铺列表。
Input:
cfss: 店铺关系
user_favu: 用户关注店铺
user_actu: 用户有动作店铺
Process:
从用户直接相关店铺出发，找这些店铺的相关店铺，再过滤。
Output:
存储CF算法产生的给用户推荐的店铺列表。cfus.kv
'''
kvg = KVEngine()
kvg.load([full_path('cfss.kv')])
kvg.load([full_path('user_favs.kv')])
kvg.load([full_path('user_actu.kv')])
kvg.load([full_path('shop_binfo.kv')])
# get shop_similarity
keys = kvg.keymatch('S\d+_CFSIMS')
shop_similarity = dict([(int(key),
dict([(int(k), float(v))
for (k, v) in kvg.getd(key).items()]))
for key in keys])
# get user_fav_shops
keys = kvg.keymatch('U\d+_FAVS')
user_fav_shops = dict([(int(key), set([int(k) for k in kvg.getl(key)]))
for key in keys])
# get blocked shop set
keys = kvg.keymatch('S\d+_BINFO')
blocked_shops = set()
for key in keys:
if kvg.getk(key, 'block') != '0':
blocked_shops.add(key_id(key))
# get user tags by fav shops
shop_tags
# get user_shops
# shop idf
# weigting and normalizing user_shops
# 给每个用户做推荐
print "Recommend for each user, total %d" % len(self.user_shops)
sys.stdout.flush()
for no, uid in enumerate(self.user_shops):
shop_weight = {} # 给该用户推荐的店铺列表及权重
shops = self.user_shops[uid] # 用户有动作的店铺列表
fav_shops = self.user_fav_shops.get(uid, {}) # 用户关注的店铺
if no % 1000 == 0:
print "%d" % no
sys.stdout.flush()
for sid in shops:
if sid not in self.shop_similarity:
continue
simi_shops = self.shop_similarity[sid]
for ssid in simi_shops:
if ssid in shop_weight:
shop_weight[ssid] += shops[sid] * simi_shops[ssid]
else:
shop_weight[ssid] = shops[sid] * simi_shops[ssid]
# 过滤shop_weight
shop_weight_new = {}
for sid in shop_weight:
# 店铺sid是否适合推荐给用户uid
if sid in fav_shops:
continue # 原本就关注
if sid in self.shop_info and self.shop_info[sid][2] != 0:
continue # 店铺的block属性非0，被屏蔽，不使用
if sid in self.shop_tags and uid in self.user_tags and \
self._tag_conflict(self.user_tags[uid], self.shop_tags[sid]):
continue # 用户关注店铺的类型与该店铺不符
shop_weight_new[sid] = shop_weight[sid]
if not shop_weight_new:
continue # 没有为此用户推荐一个店铺，都被过滤掉，不记录
# 排序，取TOP
normalize(shop_weight_new)
items = shop_weight_new.items()
items.sort(reverse=True, key=lambda x: x[1]) # sort by weight desc
self.user_recommend_list[uid] = items[:TOP_SHOP_NUM] # limit n
def compute_cfus():
'''
计算给用户推荐的店铺列表。
Input:
cfss: 店铺关系
user_favu: 用户关注店铺
user_actu: 用户有动作店铺
Process:
从用户直接相关店铺出发，找这些店铺的相关店铺，再过滤。
Output:
存储CF算法产生的给用户推荐的店铺列表。cfus.kv
'''
kvg = KVEngine()
kvg.load([full_path('cfss.kv')])
kvg.load([full_path('user_favs.kv')])
kvg.load([full_path('user_actu.kv')])
kvg.load([full_path('shop_binfo.kv')])
# get shop_similarity
keys = kvg.keymatch('S\d+_CFSIMS')
shop_similarity = dict([(int(key), dict([(int(k), float(v)) for (k, v) in kvg.getd(key).items()])) for key in keys])
# get user_fav_shops
keys = kvg.keymatch('U\d+_FAVS')
user_fav_shops = dict([(int(key), set([int(k) for k in kvg.getl(key)])) for key in keys])
# get blocked shop set
keys = kvg.keymatch('S\d+_BINFO')
blocked_shops = set()
for key in keys:
if kvg.getk(key, 'block') != '0':
blocked_shops.add(key_id(key))
# get user tags by fav shops
shop_tags
# get user_shops
# shop idf
# weigting and normalizing user_shops
# 给每个用户做推荐
print "Recommend for each user, total %d" % len(self.user_shops)
sys.stdout.flush()
for no, uid in enumerate(self.user_shops):
shop_weight = {} # 给该用户推荐的店铺列表及权重
shops = self.user_shops[uid] # 用户有动作的店铺列表
fav_shops = self.user_fav_shops.get(uid, {}) # 用户关注的店铺
if no % 1000 == 0:
print "%d" % no
sys.stdout.flush()
for sid in shops:
if sid not in self.shop_similarity:
continue
simi_shops = self.shop_similarity[sid]
for ssid in simi_shops:
if ssid in shop_weight:
shop_weight[ssid] += shops[sid] * simi_shops[ssid]
else:
shop_weight[ssid] = shops[sid] * simi_shops[ssid]
# 过滤shop_weight
shop_weight_new = {}
for sid in shop_weight:
# 店铺sid是否适合推荐给用户uid
if sid in fav_shops:
continue # 原本就关注
if sid in self.shop_info and self.shop_info[sid][2] != 0:
continue # 店铺的block属性非0，被屏蔽，不使用
if sid in self.shop_tags and uid in self.user_tags and \
self._tag_conflict(self.user_tags[uid], self.shop_tags[sid]):
continue # 用户关注店铺的类型与该店铺不符
shop_weight_new[sid] = shop_weight[sid]
if not shop_weight_new:
continue # 没有为此用户推荐一个店铺，都被过滤掉，不记录
# 排序，取TOP
normalize(shop_weight_new)
items = shop_weight_new.items()
items.sort(reverse=True, key=lambda x: x[1]) # sort by weight desc
self.user_recommend_list[uid] = items[:TOP_SHOP_NUM] # limit n
