class HybirdModel_UB(BaseModel):
def __init__(self):
BaseModel.__init__(self)
self.user_similarity = defaultdict(dict)
self.userCF = UserCF()
self.userTag = UserTagCF()
self.userLda = UserLDA()
def hybird_user_sim(self,user_songs, user_sim_file, hybird_sim_file, hybird_type='tag',theta=0.5,mix_type=0):
time_st = time.time()
self.userCF.load_user_similarity(user_sim_file,norm=1)
if hybird_type == 'tag':
self.userTag.load_user_similarity(hybird_sim_file,norm=1)
elif hybird_type == 'lda':
self.userLda.load_user_similarity(hybird_sim_file,norm=1)
#Rebuild user_similarity matrix
for uid in user_songs.keys():
candidate_user = defaultdict(float)
'''
user_sim = user_tag_sim*theta*(1+user_lda_sim*(1-theta))
greater than
user_sim= user_tag_sim * theta + user_lda_sim*(1-theta)
'''
for (vid,sim) in self.userCF.user_similarity[uid]:
if mix_type:
candidate_user[vid] += sim * theta + 1
else:
candidate_user[vid] += sim * theta
if hybird_type == 'tag':
for (vid,sim) in self.userTag.user_similarity[uid]:
if mix_type:
candidate_user[vid] *= (1+sim*(1-theta))
else:
candidate_user[vid] += sim*(1-theta)
elif hybird_type == 'lda':
for (vid,sim) in self.userLda.user_similarity[uid]:
if mix_type:
candidate_user[vid] *= (1+sim * (1-theta))
else:
candidate_user[vid] += sim * (1-theta)
#Sort sim user:
sorted_sim_user = sorted(candidate_user.items(),key=lambda x:x[1],reverse=True)
self.user_similarity[uid] = sorted_sim_user[:400]
time_ed = time.time()
logging.info('Rebuild user-similarity matrix cost:%s'%(time_ed-time_st))
def recommend(self,user_songs,user_tags,item_tags,user_k,top_n,reorder=0):
time_st = time.time()
for uid in user_songs.keys():
candidate_songs = defaultdict(float)
top_k_users = self.user_similarity[uid][:user_k]
for (vid,sim) in top_k_users:
for song in set(user_songs[vid])-set(user_songs[uid]):
candidate_songs[song] += sim
if reorder:
top_n_songs = sorted(candidate_songs.items(),key=lambda x:x[1], reverse=True)[:500] #Switch top_n*4 to 500/2015.3.8
top_n_songs = self.reorder_withItemTag(user_tags[uid],item_tags,top_n_songs)[:top_n]
else:
top_n_songs = sorted(candidate_songs.items(),key=lambda x:x[1], reverse=True)[:top_n]
top_n_songs = [song[0] for song in top_n_songs]
print "%s\t%s"%(uid,json.dumps(top_n_songs)) #输出top_n推荐结果到文件
time_ed = time.time()
self.cost_time = time_ed - time_st
def hybird_recommend_result(self,user_songs,user_k,top_n):
time_st = time.time()
for uid in user_songs.keys():
candidate_songs = defaultdict(float)
for (vid,sim) in self.userLda.user_similarity[uid][:user_k]:
for song in set(user_songs[vid])-set(user_songs[uid]):
candidate_songs[song]+= sim
for (vid,sim) in self.userTag.user_similarity[uid][:user_k]:
for song in set(user_songs[vid])-set(user_songs[uid]):
candidate_songs[song] += sim
top_n_songs = sorted(candidate_songs.items(),key=lambda x:x[1], reverse=True)[:top_n]
self.result[uid] = [song[0] for song in top_n_songs]
time_ed = time.time()
self.cost_time = time_ed - time_st
def hybird_result_withReorder(self,user_songs,user_tags,item_tags,user_k,top_n):
time_st = time.time()
for uid in user_songs.keys():
candidate_songs = defaultdict(float)
for (vid,sim) in self.userLda.user_similarity[uid][:user_k]:
for song in set(user_songs[vid])-set(user_songs[uid]):
candidate_songs[song]+= sim
for (vid,sim) in self.userTag.user_similarity[uid][:user_k]:
for song in set(user_songs[vid])-set(user_songs[uid]):
candidate_songs[song]+= sim
top_n_songs = sorted(candidate_songs.items(),key=lambda x:x[1], reverse=True)[:500]
top_n_songs = self.reorder_withItemTag(user_tags[uid],item_tags,top_n_songs)[:top_n]
self.result[uid] = [song[0] for song in top_n_songs]
time_ed = time.time()
self.cost_time = time_ed - time_st
def reorder_withItemTag(self,user_tag_distrib,items_tag_distrib,top_n_songs):
'''
@Desc:
@params[in] user_tag_distrib: dict, {tag:freq}
@params[in] items_tag_distrib: dict, {sid:{tag:freq}}
@params[in] top_n_songs: [(sid,score),]
'''
songs = set([song[0] for song in top_n_songs])
user_norm = sum([freq**2 for freq in user_tag_distrib.values()])
user_tags = set([tag for tag in user_tag_distrib.keys()])
user_song_match = defaultdict(float)
for sid in songs:
inter_tag = user_tags & set(items_tag_distrib[sid].keys())
song_norm = sum([freq**2 for freq in items_tag_distrib[sid].values()])
if len(inter_tag) == 0:
continue
for tag in inter_tag:
user_song_match[sid] += items_tag_distrib[sid][tag] * user_tag_distrib[tag]
user_song_match[sid] /= (user_norm*song_norm)**0.5
n_top_n_songs = sorted([(song[0],song[1]*(1+user_song_match[song[0]])) for song in top_n_songs],key=lambda x:x[1],reverse=True)
return n_top_n_songs
def __init__(self):
BaseModel.__init__(self)
self.user_similarity = defaultdict(dict)
self.userCF = UserCF()
self.userTag = UserTagCF()
self.userLda = UserLDA()
def __init__(self): BaseModel.__init__(self) self.user_similarity = defaultdict(dict) self.userCF = UserCF() self.userTag = UserTagCF() self.userLda = UserLDA()
class HybirdModel_UB(BaseModel):
def __init__(self):
BaseModel.__init__(self)
self.user_similarity = defaultdict(dict)
self.userCF = UserCF()
self.userTag = UserTagCF()
self.userLda = UserLDA()
def hybird_user_sim(self,
user_songs,
user_sim_file,
hybird_sim_file,
hybird_type='tag',
theta=0.5,
mix_type=0):
time_st = time.time()
self.userCF.load_user_similarity(user_sim_file, norm=1)
if hybird_type == 'tag':
self.userTag.load_user_similarity(hybird_sim_file, norm=1)
elif hybird_type == 'lda':
self.userLda.load_user_similarity(hybird_sim_file, norm=1)
#Rebuild user_similarity matrix
for uid in user_songs.keys():
candidate_user = defaultdict(float)
'''
user_sim = user_tag_sim*theta*(1+user_lda_sim*(1-theta))
greater than
user_sim= user_tag_sim * theta + user_lda_sim*(1-theta)
'''
for (vid, sim) in self.userCF.user_similarity[uid]:
if mix_type:
candidate_user[vid] += sim * theta + 1
else:
candidate_user[vid] += sim * theta
if hybird_type == 'tag':
for (vid, sim) in self.userTag.user_similarity[uid]:
if mix_type:
candidate_user[vid] *= (1 + sim * (1 - theta))
else:
candidate_user[vid] += sim * (1 - theta)
elif hybird_type == 'lda':
for (vid, sim) in self.userLda.user_similarity[uid]:
if mix_type:
candidate_user[vid] *= (1 + sim * (1 - theta))
else:
candidate_user[vid] += sim * (1 - theta)
#Sort sim user:
sorted_sim_user = sorted(candidate_user.items(),
key=lambda x: x[1],
reverse=True)
self.user_similarity[uid] = sorted_sim_user[:400]
time_ed = time.time()
logging.info('Rebuild user-similarity matrix cost:%s' %
(time_ed - time_st))
def recommend(self,
user_songs,
user_tags,
item_tags,
user_k,
top_n,
reorder=0):
time_st = time.time()
for uid in user_songs.keys():
candidate_songs = defaultdict(float)
top_k_users = self.user_similarity[uid][:user_k]
for (vid, sim) in top_k_users:
for song in set(user_songs[vid]) - set(user_songs[uid]):
candidate_songs[song] += sim
if reorder:
top_n_songs = sorted(
candidate_songs.items(), key=lambda x: x[1],
reverse=True)[:500] #Switch top_n*4 to 500/2015.3.8
top_n_songs = self.reorder_withItemTag(user_tags[uid],
item_tags,
top_n_songs)[:top_n]
else:
top_n_songs = sorted(candidate_songs.items(),
key=lambda x: x[1],
reverse=True)[:top_n]
top_n_songs = [song[0] for song in top_n_songs]
print "%s\t%s" % (uid, json.dumps(top_n_songs)) #输出top_n推荐结果到文件
time_ed = time.time()
self.cost_time = time_ed - time_st
def hybird_recommend_result(self, user_songs, user_k, top_n):
time_st = time.time()
for uid in user_songs.keys():
candidate_songs = defaultdict(float)
for (vid, sim) in self.userLda.user_similarity[uid][:user_k]:
for song in set(user_songs[vid]) - set(user_songs[uid]):
candidate_songs[song] += sim
for (vid, sim) in self.userTag.user_similarity[uid][:user_k]:
for song in set(user_songs[vid]) - set(user_songs[uid]):
candidate_songs[song] += sim
top_n_songs = sorted(candidate_songs.items(),
key=lambda x: x[1],
reverse=True)[:top_n]
self.result[uid] = [song[0] for song in top_n_songs]
time_ed = time.time()
self.cost_time = time_ed - time_st
def hybird_result_withReorder(self, user_songs, user_tags, item_tags,
user_k, top_n):
time_st = time.time()
for uid in user_songs.keys():
candidate_songs = defaultdict(float)
for (vid, sim) in self.userLda.user_similarity[uid][:user_k]:
for song in set(user_songs[vid]) - set(user_songs[uid]):
candidate_songs[song] += sim
for (vid, sim) in self.userTag.user_similarity[uid][:user_k]:
for song in set(user_songs[vid]) - set(user_songs[uid]):
candidate_songs[song] += sim
top_n_songs = sorted(candidate_songs.items(),
key=lambda x: x[1],
reverse=True)[:500]
top_n_songs = self.reorder_withItemTag(user_tags[uid], item_tags,
top_n_songs)[:top_n]
self.result[uid] = [song[0] for song in top_n_songs]
time_ed = time.time()
self.cost_time = time_ed - time_st
def reorder_withItemTag(self, user_tag_distrib, items_tag_distrib,
top_n_songs):
'''
@Desc:
@params[in] user_tag_distrib: dict, {tag:freq}
@params[in] items_tag_distrib: dict, {sid:{tag:freq}}
@params[in] top_n_songs: [(sid,score),]
'''
songs = set([song[0] for song in top_n_songs])
user_norm = sum([freq**2 for freq in user_tag_distrib.values()])
user_tags = set([tag for tag in user_tag_distrib.keys()])
user_song_match = defaultdict(float)
for sid in songs:
inter_tag = user_tags & set(items_tag_distrib[sid].keys())
song_norm = sum(
[freq**2 for freq in items_tag_distrib[sid].values()])
if len(inter_tag) == 0:
continue
for tag in inter_tag:
user_song_match[
sid] += items_tag_distrib[sid][tag] * user_tag_distrib[tag]
user_song_match[sid] /= (user_norm * song_norm)**0.5
n_top_n_songs = sorted([(song[0], song[1] *
(1 + user_song_match[song[0]]))
for song in top_n_songs],
key=lambda x: x[1],
reverse=True)
return n_top_n_songs