def get_similarity(userID, itemID, DATA): # compute sim(u_j, i_k)
userClass = PHASE2.get_user_key_class(userID, DATA['user_key_word'], DATA['keyword_class'])
itemClass = PHASE2.get_user_key_class(itemID, DATA['itemKey'], DATA['keyword_class'])
if userClass == None:
print 'no class in userClass', userID
return 1
if itemClass == None:
print 'no class in itemClass', itemID
return 1
n = max(len(userClass.keys()), len(itemClass.keys())) +1
dist = 0
for CLASS in range(n):
if CLASS in userClass and CLASS in itemClass:
dist += (userClass[CLASS] - itemClass[CLASS])**2
elif CLASS in userClass:
dist += userClass[CLASS]**2
elif CLASS in itemClass:
dist += itemClass[CLASS]**2
return 2/(1 + math.exp(dist**0.5))
def GRADE(userID, itemID, weight, fweight, DATA, userClass):
# weight = w1
# fweight = [fw1,fw2]
#partial = [0,0]
W = 0
INFO = {}
INFO['GRADE'] = 0
INFO['WEIGHT'] = 0
INFO['h - s'] = 0
#INFO['partial'] = partial
'''SPECIFY EVERY DATASET'''
# itemCate = DATA['itemCate']
# user_accepted_cate = DATA['user_accepted_cate']
# user_action = DATA['user_action']
# user_sns = DATA['user_sns']
# cateItem = DATA['cateItem']
# user_key_word = DATA['user_key_word']
# keyword_class = DATA['keyword_class']
# class_cate = {class:{cate:weight,...},...}
'''GET CATEGORY OF THE ITEM'''
# function test OK
itemCate = GET.get_item_cate(itemID, DATA['itemCate'])
if itemCate == None:
print 'None in ITEMCATE at PHASE1' + ', userID: ' + userID + ', itemID: ' + itemID
'''MATCH THE USER CATEGORY IN PHASE 1 AND RETURN WEIGHT 1'''
# get userCate in phase 1
# userCate = [cate,...], funtion test OK
userCate = GET.get_accepted(userID, DATA['user_accepted_cate'])
if userCate == None:
print 'None in USERCATE at PHASE1' + ', userID: ' + userID + ', itemID: ' + itemID
else:
if itemCate in userCate:
W = 1
'''NO MATCH: MATCH THE USER CATEGORY IN PHASE 2 AND RETURN WEIGHT'''
if W == 0:
print 'START PHASE 2,', 'user', userID, 'item', itemID
# user_key_class = {class:{key:weight,...},...}, function test DANTE
user_key_class = PHASE2.get_user_key_class(userID, DATA['user_key_word'], DATA['keyword_class'])
if user_key_class == None:
print 'None in USER_KEY_CLASS in PHASE2' + ', userID: ' + userID + ', itemID: ' + itemID
else:
# userCate = {cate:weight,...}, function test DANTE
userCate = PHASE2.get_cate_weight(user_key_class, DATA['class_cate'])
#print userID, user_key_class
#print userCate
if userCate == None:
print 'None in USERCATE at PHASE2' + ', userID: ' + userID + ', itemID: ' + itemID
elif itemCate in userCate and W == 0:
W = userCate[itemCate]
'''NO MATCH AGAIN: MATCH THE USER CATEGORY IN PHASE 3 AND RETURN WEIGHT'''
if W == 0:
print 'START PHASE 3,', 'user', userID, 'item', itemID
# get followee
# Neighbor = [followee,...], function test OK
Neighbor = GET.get_followee(userID, DATA['user_sns'])
if Neighbor == None:
print 'None in NEIGHBOR at PHASE3' + ', userID: ' + userID + ', itemID: ' + itemID
return INFO
# get followee's category in phase 1(WRONG) and 2
NeighborNum = len(Neighbor) # average
for followeeID in Neighbor:
# get followeeCate in phase1(WRONG) and phase 2, function test OK
followeeCate1 = GET.get_accepted(followeeID, DATA['user_accepted_cate'])
# user_key_class = {class:{key:weight,...},...}, function test DANTE
user_key_class = PHASE2.get_user_key_class(userID, DATA['user_key_word'], DATA['keyword_class'])
# userCate = {cate:weight,...}, function test DANTE
followeeCate2 = PHASE2.get_cate_weight(user_key_class, DATA['class_cate'])
# weight return with caculation of familiarity, using fweight
if followeeCate2 == None and followeeCate1 == None:
print 'None in FOLLOWEECATE at PHASE3' + ', userID: ' + userID + ', itemID: ' + itemID
return INFO
if followeeCate1 != None and itemCate in followeeCate1:
wPhase2 = 1 # tbd: get weight in phase 1 and 2
elif followeeCate2 != None and itemCate in followeeCate2:
wPhase2 = followeeCate2[itemCate]
else:
print 'None ITEMCATE in FOLLOWEECATE at PHASE3' + ', userID: ' + userID + ', itemID: ' + itemID
return INFO
# function test OK
temp = GET.get_action(userID, followeeID, DATA['user_action'])
if temp == None:
W = wPhase2 # tbd
else:
at, retweet, comment = temp
W += (fweight[0] * sigmoid(at) + fweight[1] * sigmoid(retweet) + fweight[2] * sigmoid(comment)) * wPhase2 / NeighborNum
#partial[0] += (sigmoid(at) - sigmoid(comment)) * wPhase2 / NeighborNum
#partial[1] += (sigmoid(retweet) - sigmoid(comment)) * wPhase2 / NeighborNum
'''TO BE SPECIFIED: PHASE2 AND PHASE3 EXCLUSIVE?'''
if itemCate in userCate and W != 1:
if userCate[itemCate] >= W:
W = userCate[itemCate]
#partial = [0,0]
'''IF MATCH: RETURN THE HOT GRADE AND SIMILARITY'''
# get hot rank
# return the hot rank of item in category, function test OK
hot = get_hot_rank(itemID, DATA['itemCate'], DATA['cateItem'])
# get similarity
similarity = get_similarity(userID, itemID, DATA) # function test OK
'''GRADING: NO MATCH RETURN -1, ELSE GRADE = 2*W(w1*h+(1-w1)*s)-1'''
if userClass >= 0:
INFO['GRADE'] = 2 * zoom(W, 100) * (weight * hot + (1 - weight) * similarity) - 1 # no time info
INFO['WEIGHT'] = zoom(W, 100)
INFO['h - s'] = hot - similarity
#print 'user', userID, 'item', itemID, 'hot', hot, 'similarity', similarity, 'WEIGHT', INFO['WEIGHT'], 'W', W, 'GRADE', INFO['GRADE']
#INFO['partial'] = partial
else:
INFO['GRADE'] = (1 + zoom(W, 100)) * hot - 1
return INFO
