协作型过滤应用——提供推荐

一、准备数据

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}}

二、相似度评价方法

#计算两个向量的欧氏距离来评价用户的相似度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 si])    return 1/(1+sqrt(sum_of_squares))#使得越相似,距离越近,评价指数越高#计算两个向量的皮尔逊相关系数,数值越大,相似度越高def sim_pearson(prefs,person1,person2):    si={}    for item in prefs[person1]:        if item in prefs[person2]:            si[item]=1    n=len(si)    if n==0:        return 1    sum1=sum([prefs[person1][it] for it in si])    sum2=sum([prefs[person2][it] for it in si])    sum1sq=sum([pow(prefs[person1][it],2) for it in si])    sum2sq=sum([pow(prefs[person2][it],2) for it in si])    pSum=sum([prefs[person1][it]*prefs[person2][it] for it in si])    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#返回相似度最高的前n个向量def topMatches(prefs,person,n=5,similarity=sim_pearson):    scores=[(similarity(prefs,person,other),other) for other in prefs if other!=person]    scores.sort()    scores.reverse()    return scores[0:n]

三、根据相似度评价加权推荐

#根据用户的相似度来加权电影评分进行推荐def getRecommendations(prefs,person,similarity=sim_pearson):    totals={}    simSums={}    for other in prefs:        if other==person:            continue        sim=similarity(prefs,person,other)#得到相似度评价值        if sim<=0:            continue        for item in prefs[other]:            if item not in prefs[person] or prefs[person][item]==0:                totals.setdefault(item,0)                totals[item]+=prefs[other][item]*sim#用相似度加权评分                simSums.setdefault(item,0)                simSums[item]+=sim    rankings=[(total/simSums[item],item) for item,total in totals.items()]    rankings.sort()    rankings.reverse()    return rankings#返回排好序的推荐列表#将数据中人和电影的位置互换,可以根据电影的相似度来推荐品味相似的用户def transformPrefs(prefs):    result={}    for person in prefs:        for item in prefs[person]:            result.setdefault(item,{})            result[item][person]=prefs[person][item]    return result

四、测试

print(topMatches(critics,'Toby',n=3))print(getRecommendations(critics,'Toby'))print(getRecommendations(critics,'Toby',similarity=sim_distance))movies=transformPrefs(critics)print(topMatches(movies,'Superman Returns'))print(getRecommendations(movies,'Just My Luck'))print(getRecommendations(movies,'Just My Luck',similarity=sim_distance))

结果输出

[(0.9912407071619299, 'Lisa Rose'), (0.9244734516419049, 'Mick LaSalle'), (0.8934051474415647, 'Claudia Puig')][(3.3477895267131013, 'The Night Listener'), (2.832549918264162, 'Lady in the Water'), (2.530980703765565, 'Just My Luck')][(3.4571286944914235, 'The Night Listener'), (2.778584003814924, 'Lady in the Water'), (2.422482042361917, 'Just My Luck')][(0.6579516949597695, 'You, Me and Dupree'), (0.4879500364742689, 'Lady in the Water'), (0.11180339887498941, 'Snakes on a Plane'), (-0.1798471947990544, 'The Night Listener'), (-0.42289003161103106, 'Just My Luck')][(4.0, 'Michael Phillips'), (3.0, 'Jack Matthews')][(3.5810970647618663, 'Jack Matthews'), (3.2059731906295044, 'Michael Phillips'), (2.936629402844435, 'Toby')]