(一)基于内容的
暂时不分析
(二)协同过滤
一个协同过滤算法通常的做法是对一大群人进行搜索,从中找到与我们品味相近的一小群人,算法会对这些人所偏爱的其他内容进行考查,并把他们组合成一个经过排名的推荐列表。
1,首先我们先找与我们品味相同的用户
我们常用的两套计算相似度评价值得体系: 欧几里德距离和 皮尔逊相关度
例子:
# set of movies 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}}
欧几里德距离
from math import sqrt # Returns a distance-based similarity score for person1 and person2 def sim_distance(prefs,person1,person2): # Get the list of shared_items si={} for item in prefs[person1]: if item in prefs[person2]: si[item]=1 # if they have no ratings in common, return 0 if len(si)==0: return 0 # Add up the squares of all the differences 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+sum_of_squares)
运行:
>>> import myset
>>> myset.sim_distance(myset.critics,'Lisa Rose','Gene Seymour')
0.14814814814814814
上述执行过程给出了Lisa Rose和Gene Seymour之间的相似度评价
皮尔逊相关度
如果某人总是倾向于给出比另一个人更高的分值,而二者的分值之差又始终保持一致,则他们依然可能会存在很好的相关性,前面的欧几里德距离就显得不合适了。我们看下皮尔逊相关度评价。
def sim_pearson(prefs,p1,p2): # Get the list of mutually rated items si={} for item in prefs[p1]: if item in prefs[p2]: si[item]=1 # if they are no ratings in common, return 0 if len(si)==0: return 0 # Sum calculations n=len(si) # Sums of all the preferences sum1=sum([prefs[p1][it] for it in si]) sum2=sum([prefs[p2][it] for it in si]) # Sums of the squares sum1Sq=sum([pow(prefs[p1][it],2) for it in si]) sum2Sq=sum([pow(prefs[p2][it],2) for it in si]) # Sum of the products pSum=sum([prefs[p1][it]*prefs[p2][it] for it in si]) # Calculate r (Pearson score) num=pSum-(sum1*sum2/n) den=sqrt((sum1Sq-pow(sum1,2)/n)*(sum2Sq-pow(sum2,2)/n)) if den==0: return 0 r=num/den return r
发现一篇介绍推荐引擎的文章写得很不错
http://www.oschina.net/question/12_17362