作为个人记录之用，主要是将代码及其注释贴出来。
from math import sqrt 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 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]]) #威尔逊相关度 绘制一条尽可能靠近地图上所有的坐标点 称为最佳拟合线 def simPerson(prefs, p1, p2): #得到双方都评价过的物品列表 si = {} for item in prefs[p1]: if item in prefs[p2]: si[item] = 1 n = len(si) if n == 0: return -1 sum1 = sum([prefs[p1][it] for it in si]) sum2 = sum([prefs[p2][it] for it in si]) #求平方和 sum1sq = sum([pow(prefs[p1][it], 2) for it in si]) sum2sq = sum([pow(prefs[p2][it], 2) for it in si]) #求乘积之和 pSum = sum([prefs[p1][it]*prefs[p2][it] for it in si]) #计算皮尔逊评价值 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 def topMatches(prefs, person, n=5, similarity=simPerson): scores = [] # scores=[(similarity(prefs,person,other),other) # for other in prefs if other != person] for other in prefs: if other != person: scores.append((similarity(prefs, person, other), other)) scores.sort() scores.reverse() print(scores[0:n]) return scores[0:n] topMatches(critics, 'Toby', n=6) def get_recommendation(prefs, person, similarity=simPerson): totals = {} simSums = {} for other in prefs: if other == person: continue sim = similarity(prefs, person, other) 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 def transformPrefs(prefs): result = {} for person in prefs: for item in prefs[person]: result.setdefault(item, {}) #字典中如果有item没有这个key,就插入这个key并赋值，并返回result的值（默认为None） #如果有这个key则返回相应的value #作用在于将所有的电影名添加 result[item][person] = prefs[person][item] return result import pydelicious print(pydelicious.get_popular(tag='programming')) def calculateSimlarItems(prefs, n = 10): result = {} #以物品为中心对偏好矩阵实施倒置处理 itemPrefs = transformPrefs(prefs) c = 0 for item in itemPrefs: c += 1 if c % 100 == 0: d = c / len(itemPrefs) print(d) scores = topMatches(itemPrefs, item, n=n, similarity=sim_distance) result[item] = scores return result def getRcommendeditems(prefs, itemMatch, user): userRatings = prefs[user] scores = {} totalSim = {} for(item, rating) in userRatings.items(): #循环遍历由当前用户评分的物品 for (similarity, item2) in itemMatch[item]:#循环遍历与当前物品相近的物品 if item2 in userRatings: continue scores.setdefault(item2, 0) scores[item2] += similarity * rating #相似度*当前物品的评分 对某部电影有一个评分，找到相似的并求出相似度，推算出评价分 totalSim.setdefault(item2, 0) totalSim[item2] += similarity rankings = [(scores / totalSim[item], item) for item, score in scores.items()] #此时的item为相似的物品，score为加权分 rankings.sort() rankings.reverse() return rankings