提供推荐
- 计算两个人的相似度
- 本来是推荐平均评分较高的作品，考虑到两个人的爱好相似程度，对评分根据相似度进行加权平均
计算相似度：
- 欧几里得距离
- pearson相关度
critics={'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}} 计算相关度
pearson相关系数计算公式（）
from math import sqrt# 欧几里得距离评价def sim_distance(prefs, person1, person2): si = {} for item in prefs[person1]: if item in prefs[person2]: si[item] = 1 if len(si) == 0: return 0 sum_of_squares = sum([pow(prefs[person1][item] - prefs[person2][item], 2) for item in prefs[person1] if item in prefs[person2]]) return 1 / (1 + sqrt(sum_of_squares))# 皮尔逊相关度评价def sim_pearson(prefs, person1, person2): # 得到两者评价过的相同商品 si = {} for item in prefs[person1]: if item in prefs[person2]: si[item] = 1 n = len(si) # 如果两个用户之间没有相似之处则返回1 if n == 0: return 1 # 对各自的所有偏好求和 sum1 = sum([prefs[person1][item] for item in si]) sum2 = sum([prefs[person2][item] for item in si]) # 求各自的平方和 sum1_square = sum([pow(prefs[person1][item], 2) for item in si]) sum2_square = sum([pow(prefs[person2][item], 2) for item in si]) # 求各自的乘积的平方 sum_square = sum([prefs[person1][item] * prefs[person2][item] for item in si]) # 计算pearson相关系数 den = sqrt((sum1_square - pow(sum1, 2) / n) * (sum2_square - pow(sum2, 2) / n)) if den == 0: return 0 return (sum_square - (sum1 * sum2/n)) / den print sim_distance(critics, 'Lisa Rose', 'Gene Seymour')
0.294298055086
print sim_pearson(critics, 'Lisa Rose', 'Gene Seymour')
0.396059017191
评论者打分
def topMatches(prefs, person, n = 5, simlarity = sim_pearson): scores = [(simlarity(prefs, person, other), other) for other in prefs if other != person] # 对列表进行排序，评价高者排在前面 scores.sort() scores.reverse() # 取指定个数的（不需要判断n的大小，因为python中的元组可以接受正、负不在范围内的index） return scores[0:n]
寻找和“Toby”有相似偏好的人，取前3个
topMatches(critics, 'Toby', n = 3)
[(0.9912407071619299, 'Lisa Rose'), (0.9244734516419049, 'Mick LaSalle'), (0.8934051474415647, 'Claudia Puig')]
# 利用其他所有人的加权平均给用户推荐def get_recommendations(prefs, person, similarity=sim_pearson): # 其他用户对某个电影的评分加权之后的总和 totals = {} # 其他用户的相似度之和 sim_sums = {} for other in prefs: # 不和自己比较 if other == person: continue # 求出相似度 sim = similarity(prefs, person, other) # 忽略相似度小于等于情况0的 if sim <= 0: continue # 获取other所有的评价过的电影评分的加权值 for item in prefs[other]: # 只推荐用户没看过的电影 if item not in prefs[person] or prefs[person][item] == 0: #print item # 设置默认值 totals.setdefault(item, 0) # 求出该电影的加权之后的分数之和 totals[item] += prefs[other][item] * sim # 求出各个用户的相似度之和 sim_sums.setdefault(item, 0) sim_sums[item] += sim # 对于加权之后的分数之和取平均值 rankings = [(total / sim_sums[item], item) for item, total in totals.items()] # 返回经过排序之后的列表 rankings.sort() rankings.reverse() return rankings 给出Toby的电影推荐列表
print get_recommendations(critics, 'Toby')print get_recommendations(critics, 'Toby', similarity=sim_distance)
[(3.3477895267131013, 'The Night Listener'), (2.8325499182641614, 'Lady in the Water'), (2.5309807037655645, 'Just My Luck')][(3.457128694491423, 'The Night Listener'), (2.778584003814924, 'Lady in the Water'), (2.4224820423619167, 'Just My Luck')]