协同过滤算法+源代码+文档说明

#-*-coding:utf-8-*- ''''' Created on 2016年5月2日 @author: Gamer Think ''' from math import sqrt fp = open("uid_score_bid","r") users = {} for line in open("uid_score_bid"): lines = line.strip().split(",") if lines[0] not in users: users[lines[0]] = {} users[lines[0]][lines[2]]=float(lines[1]) #----------------新增代码段END---------------------- class recommender: #data：数据集，这里指users #k：表示得出最相近的k的近邻 #metric：表示使用计算相似度的方法 #n：表示推荐book的个数 def __init__(self, data, k=3, metric='pearson', n=12): self.k = k self.n = n self.username2id = {} self.userid2name = {} self.productid2name = {} self.metric = metric if self.metric == 'pearson': self.fn = self.pearson if type(data).__name__ == 'dict': self.data = data def convertProductID2name(self, id): if id in self.productid2name: return self.productid2name[id] else: return id #定义的计算相似度的公式，用的是皮尔逊相关系数计算方法 def pearson(self, rating1, rating2): sum_xy = 0 sum_x = 0 sum_y = 0 sum_x2 = 0 sum_y2 = 0 n = 0 for key in rating1: if key in rating2: n += 1 x = rating1[key] y = rating2[key] sum_xy += x * y sum_x += x sum_y += y sum_x2 += pow(x, 2) sum_y2 += pow(y, 2) if n == 0: return 0 #皮尔逊相关系数计算公式 denominator = sqrt(sum_x2 - pow(sum_x, 2) / n) * sqrt(sum_y2 - pow(sum_y, 2) / n) if denominator == 0: return 0 else: return (sum_xy - (sum_x * sum_y) / n) / denominator def computeNearestNeighbor(self, username): distances = [] for instance in self.data: if instance != username: distance = self.fn(self.data[username],self.data[instance]) distances.append((instance, distance)) distances.sort(key=lambda artistTuple: artistTuple[1],reverse=True) return distances #推荐算法的主体函数 def recommend(self, user): #定义一个字典，用来存储推荐的书单和分数 recommendations = {} #计算出user与所有其他用户的相似度，返回一个list nearest = self.computeNearestNeighbor(user) # print nearest userRatings = self.data[user] # print userRatings totalDistance = 0.0 #得住最近的k个近邻的总距离 for i in range(self.k): totalDistance += nearest[i][1] if totalDistance==0.0: totalDistance=1.0 #将与user最相近的k个人中user没有看过的书推荐给user，并且这里又做了一个分数的计算排名 for i in range(self.k): #第i个人的与user的相似度，转换到[0,1]之间 weight = nearest[i][1] / totalDistance #第i个人的name name = nearest[i][0] #第i个用户看过的书和相应的打分 neighborRatings = self.data[name] for artist in neighborRatings: if not artist in userRatings: if artist not in recommendations: recommendations[artist] = (neighborRatings[artist] * weight) else: recommendations[artist] = (recommendations[artist]+ neighborRatings[artist] * weight) recommendations = list(recommendations.items()) recommendations = [(self.convertProductID2name(k), v)for (k, v) in recommendations] #做了一个排序 recommendations.sort(key=lambda artistTuple: artistTuple[1], reverse = True) return recommendations[:self.n],nearest def adjustrecommend(id): bookid_list = [] r = recommender(users) k,nearuser = r.recommend("%s" % id) for i in range(len(k)): bookid_list.append(k[i][0]) return bookid_list,nearuser[:15] #bookid_list推荐书籍的id，nearuser[:15]最近邻的15个用户 bookid_list,near_list = adjustrecommend("changanamei") print ("bookid_list:",bookid_list) print ("near_list:",near_list)