基于物品—SVD餐馆评分估计值评分|

【基于物品—SVD餐馆评分估计值】敢说敢作敢为，无怨无恨无悔。这篇文章主要讲述基于物品—SVD餐馆评分估计值相关的知识，希望能为你提供帮助。

文章目录

??1 导入模块并创建数据集??
??2 定义距离函数??
??3 基于物品相似度, 计算用户对物体的评分估计值??
??4 基于 SVD, 计算用户对物体的评分估计值??

1 导入模块并创建数据集

from numpy import *
from numpy import linalg as la

def loadExData():
return[[0, 0, 0, 2, 2],
[0, 0, 0, 3, 3],
[0, 0, 0, 1, 1],
[1, 1, 1, 0, 0],
[2, 2, 2, 0, 0],
[5, 5, 5, 0, 0],
[1, 1, 1, 0, 0]]

def loadExData2():
return[[0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 5],
[0, 0, 0, 3, 0, 4, 0, 0, 0, 0, 3],
[0, 0, 0, 0, 4, 0, 0, 1, 0, 4, 0],
[3, 3, 4, 0, 0, 0, 0, 2, 2, 0, 0],
[5, 4, 5, 0, 0, 0, 0, 5, 5, 0, 0],
[0, 0, 0, 0, 5, 0, 1, 0, 0, 5, 0],
[4, 3, 4, 0, 0, 0, 0, 5, 5, 0, 1],
[0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 4],
[0, 0, 0, 2, 0, 2, 5, 0, 0, 1, 2],
[0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0],
[1, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0]]

def loadExData3():
# 利用SVD提高推荐效果，菜肴矩阵
return[[2, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],
[0, 0, 0, 0, 0, 0, 0, 1, 0, 4, 0],
[3, 3, 4, 0, 3, 0, 0, 2, 2, 0, 0],
[5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0],
[4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 5],
[0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 4],
[0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0],
[0, 0, 0, 3, 0, 0, 0, 0, 4, 5, 0],
[1, 1, 2, 1, 1, 2, 1, 0, 4, 5, 0]]

2 定义距离函数

def ecludSim(inA,inB):#计算欧几里得相似度（范数）
return 1.0/(1.0 + la.norm(inA - inB))

def pearsSim(inA,inB): #皮尔森相似度
if len(inA) < 3 : return 1.0
return 0.5+0.5*corrcoef(inA, inB, rowvar = 0)[0][1]

def cosSim(inA,inB): #cos相似度
num = float(inA.T*inB)
denom = la.norm(inA)*la.norm(inB)
return 0.5+0.5*(num/denom)

3 基于物品相似度, 计算用户对物体的评分估计值

def standEst(dataMat, user, simMeas, item):#estimate：评估器（数据矩阵，用户，相似度，菜种类）
n = shape(dataMat)[1]#取列
simTotal = 0.0; ratSimTotal = 0.0
for j in range(n):
userRating = dataMat[user,j]
if userRating == 0: continue
overLap = nonzero(logical_and(dataMat[:,item].A> 0, \\
dataMat[:,j].A> 0))[0]#如果第j个菜和item同时被人吃（返回bool的Ture）
if len(overLap) == 0: similarity = 0
else: similarity = simMeas(dataMat[overLap,item], \\
dataMat[overLap,j])
print(the %d and %d similarity is: %f % (item, j, similarity))
simTotal += similarity
ratSimTotal += similarity * userRating
if simTotal == 0: return 0
else: return ratSimTotal/simTotal

4 基于 SVD, 计算用户对物体的评分估计值

def svdEst(dataMat, user, simMeas, item):
n = shape(dataMat)[1]
simTotal = 0.0; ratSimTotal = 0.0
U,Sigma,VT = la.svd(dataMat)
# 在SVD分解之后，我们只利用包含了90%能量值的奇异值，
Sig4 = mat(eye(4)*Sigma[:4]) # eye生成单位矩阵，整个语句将奇异值变为矩阵的形式
# 利用U矩阵将物品转换到低维空间中，构建转换后的物品(物品+4个主要的特征)
xformedItems = dataMat.T * U[:,:4] * Sig4.I#变换坐标后的形式
for j in range(n): # #变换坐标后的形式# 列数现在变成了行数
userRating = dataMat[user,j]
if userRating == 0 or j==item: continue
similarity = simMeas(xformedItems[item,:].T,\\
xformedItems[j,:].T)# 因为矩阵已经转置了，所以每行代表商品
print(the %d and %d similarity is: %f % (item, j, similarity))
simTotal += similarity
ratSimTotal += similarity * userRating
if simTotal == 0: return 0
else: return ratSimTotal/simTotal

def recommend(dataMat, user, N=3, simMeas=cosSim, estMethod=standEst):
unratedItems = nonzero(dataMat[user,:].A==0)[1]#find unrated items
if len(unratedItems) == 0: return you rated everything
itemScores = []
for item in unratedItems:
estimatedScore = estMethod(dataMat, user, simMeas, item)
itemScores.append((item, estimatedScore))
return sorted(itemScores, key=lambda jj: jj[1], reverse=True)[:N]

def printMat(inMat, thresh=0.8):
for i in range(32):
for k in range(32):
if float(inMat[i,k]) > thresh:
print(1, end=)
else: print(0, end=)
print()

myMat = mat(loadExData3())
rec_res = recommend(myMat, 1, estMethod=svdEst)
print(rec_res)

rec_res = recommend(myMat, 1, estMethod=svdEst, simMeas=pearsSim)
print(rec_res)

rec_res = recommend(myMat, 2)
print(rec_res)
# 注释: loadExData3函数是新增的