Collaborative Filtering协同过滤推荐算法

港控/mmm° 2022-09-21 05:43 256阅读 0赞

from math import sqrt
    
    dataset={
    			'Lisa Rose': {'Lady in the Water': 2.5, 
    							'Snakes on a Plane': 3.5,
    							'Just My Luck': 3.0, 
    							'Superman Returns': 3.5, 
    							'You, Me and Dupree': 2.5,
    							'The Night Listener': 3.0},
    			'Gene Seymour': {'Lady in the Water': 3.0, 
    							'Snakes on a Plane': 3.5,
    							'Just My Luck': 1.5,
    							 'Superman Returns': 5.0, 
    							 'The Night Listener': 3.0,
    							'You, Me and Dupree': 3.5},
    
    			'Michael Phillips': {'Lady in the Water': 2.5, 
    								'Snakes on a Plane': 3.0,
    								'Superman Returns': 3.5,
    								 'The Night Listener': 4.0},
    			'Claudia Puig': {'Snakes on a Plane': 3.5, 
    							'Just My Luck': 3.0,
    							'The Night Listener': 4.5, 
    							'Superman Returns': 4.0,
    							'You, Me and Dupree': 2.5},
    			'Mick LaSalle': {'Lady in the Water': 3.0, 
    							'Snakes on a Plane': 4.0,
    							'Just My Luck': 2.0, 
    							'Superman Returns': 3.0, 
    							'The Night Listener': 3.0,
    							'You, Me and Dupree': 2.0},
    			'Jack Matthews': {'Lady in the Water': 3.0, 
    							'Snakes on a Plane': 4.0,
    							'The Night Listener': 3.0, 
    							'Superman Returns': 5.0, 
    							'You, Me and Dupree': 3.5},
    			'Toby': {'Snakes on a Plane':4.5,
    					'You, Me and Dupree':1.0,
    					'Superman Returns':4.0}}
    					
    def similarity_score(person1,person2):
    	
    	# Returns ratio Euclidean distance score of person1 and person2 
    
    	both_viewed = {}		# To get both rated items by person1 and person2
    
    	for item in dataset[person1]:
    		if item in dataset[person2]:
    			both_viewed[item] = 1
    
    		# Conditions to check they both have an common rating items	
    		if len(both_viewed) == 0:
    			return 0
    
    		# Finding Euclidean distance 
    		sum_of_eclidean_distance = []	
    
    		for item in dataset[person1]:
    			if item in dataset[person2]:
    				sum_of_eclidean_distance.append(pow(dataset[person1][item] - dataset[person2][item],2))
    		sum_of_eclidean_distance = sum(sum_of_eclidean_distance)
    
    		return 1/(1+sqrt(sum_of_eclidean_distance))
    
    
    
    def pearson_correlation(person1,person2):
    
    	# To get both rated items
    	both_rated = {}
    	for item in dataset[person1]:
    		if item in dataset[person2]:
    			both_rated[item] = 1
    
    	number_of_ratings = len(both_rated)		
    	
    	# Checking for number of ratings in common
    	if number_of_ratings == 0:
    		return 0
    
    	# Add up all the preferences of each user
    	person1_preferences_sum = sum([dataset[person1][item] for item in both_rated])
    	person2_preferences_sum = sum([dataset[person2][item] for item in both_rated])
    
    	# Sum up the squares of preferences of each user
    	person1_square_preferences_sum = sum([pow(dataset[person1][item],2) for item in both_rated])
    	person2_square_preferences_sum = sum([pow(dataset[person2][item],2) for item in both_rated])
    
    	# Sum up the product value of both preferences for each item
    	product_sum_of_both_users = sum([dataset[person1][item] * dataset[person2][item] for item in both_rated])
    
    	# Calculate the pearson score
    	numerator_value = product_sum_of_both_users - (person1_preferences_sum*person2_preferences_sum/number_of_ratings)
    	denominator_value = sqrt((person1_square_preferences_sum - pow(person1_preferences_sum,2)/number_of_ratings) * (person2_square_preferences_sum -pow(person2_preferences_sum,2)/number_of_ratings))
    	if denominator_value == 0:
    		return 0
    	else:
    		r = numerator_value/denominator_value
    		return r 
    
    def most_similar_users(person,number_of_users):
    	# returns the number_of_users (similar persons) for a given specific person.
    	scores = [(pearson_correlation(person,other_person),other_person) for other_person in dataset if  other_person != person ]
    	
    	# Sort the similar persons so that highest scores person will appear at the first
    	scores.sort()
    	scores.reverse()
    	return scores[0:number_of_users]
    
    def user_reommendations(person):
    
    	# Gets recommendations for a person by using a weighted average of every other user's rankings
    	totals = {}
    	simSums = {}
    	rankings_list =[]
    	for other in dataset:
    		# don't compare me to myself
    		if other == person:
    			continue
    		sim = pearson_correlation(person,other)
    		#print ">>>>>>>",sim
    
    		# ignore scores of zero or lower
    		if sim <=0: 
    			continue
    		for item in dataset[other]:
    
    			# only score movies i haven't seen yet
    			if item not in dataset[person] or dataset[person][item] == 0:
    
    			# Similrity * score
    				totals.setdefault(item,0)
    				totals[item] += dataset[other][item]* sim
    				# sum of similarities
    				simSums.setdefault(item,0)
    				simSums[item]+= sim
    
    		# Create the normalized list
    
    	rankings = [(total/simSums[item],item) for item,total in totals.items()]
    	rankings.sort()
    	rankings.reverse()
    	# returns the recommended items
    	recommendataions_list = [recommend_item for score,recommend_item in rankings]
    	return recommendataions_list
    		
    
    print user_reommendations('Toby')

[探索推荐引擎内部的秘密，第 2 部分: 深入推荐引擎相关算法 - 协同过滤][2 _ _ -]

[探索推荐引擎内部的秘密，第 3 部分: 深入推荐引擎相关算法 - 聚类][3 _ _ -]

# [memory-based 协同过滤（CF）方法][memory-based _CF] #

# [推荐方法总结][Link 1] #

[机器学习相关——协同过滤][Link 2]

[Collaborative filtering 学习总结][Collaborative filtering]

[如何利用用户标签数据][Link 3]

[基于标签的推荐系统][Link 4]

[基于用户的最近邻推荐][Link 5]

[2 _ _ -]: http://www.ibm.com/developerworks/cn/web/1103_zhaoct_recommstudy2/index.html
[3 _ _ -]: http://www.ibm.com/developerworks/cn/web/1103_zhaoct_recommstudy3/index.html
[memory-based _CF]: http://www.cnblogs.com/ywl925/p/3545587.html
[Link 1]: http://www.cnblogs.com/ywl925/p/3545439.html
[Link 2]: http://www.cnblogs.com/luchen927/archive/2012/02/01/2325360.html
[Collaborative filtering]: http://www.cnblogs.com/liangxing/archive/2010/03/29/1699423.html
[Link 3]: http://www.ituring.com.cn/article/725
[Link 4]: http://www.ituring.com.cn/article/786#
[Link 5]: http://blog.csdn.net/lifeitengup/article/details/12949967