diff --git a/src/python/14.SVD/svdRec.py b/src/python/14.SVD/svdRec.py
index f705e37e..5566816b 100644
--- a/src/python/14.SVD/svdRec.py
+++ b/src/python/14.SVD/svdRec.py
@@ -1,30 +1,74 @@
-# coding=utf-8
-def loadExData():
-    return[[1,1,1,0,0],
-           [2,2,2,0,0],
-           [1,1,1,0,0],
-           [5,5,5,0,0],
-           [1,1,0,2,2],
-           [0,0,0,3,3],
-           [0,0,0,1,1]]
+#!/usr/bin/python
+# encoding: utf-8
 
-from numpy import * 
+from numpy import *
 from numpy import linalg as la
+
+
+def loadExData():
+    return[[1, 1, 1, 0, 0],
+           [2, 2, 2, 0, 0],
+           [1, 1, 1, 0, 0],
+           [5, 5, 5, 0, 0],
+           [1, 1, 0, 2, 2],
+           [0, 0, 0, 3, 3],
+           [0, 0, 0, 1, 1]]
+
+
 # 欧氏距离相似度，假定inA和inB 都是列向量
 # 计算向量的第二范式，相当于计算了欧氏距离
-def ecludSim(inA,inB):
+def ecludSim(inA, inB):
     return 1.0/(1.0 + la.norm(inA - inB))
 
+
 # pearsSim()函数会检查是否存在3个或更多的点。
 # corrcoef直接计算皮尔逊相关系数
-def pearsSim(inA,inB):
+def pearsSim(inA, inB):
     # 如果不存在，该函数返回1.0，此时两个向量完全相关。
-    if len(inA)< 3 :return 1.0
-    return 0.5 + 0.5*corrcoef(inA,inB,rowvar = 0)[0][1]
+    if len(inA) < 3:
+        return 1.0
+    return 0.5 + 0.5*corrcoef(inA, inB, rowvar=0)[0][1]
+
 
 # 计算余弦相似度
-def cosSim(inA,inB):
+def cosSim(inA, inB):
     num = float(inA.T*inB)
     denom = la.norm(inA)*la.norm(inB)
-    return 0.5 +0.5*(num/denom)
+    return 0.5 + 0.5*(num/denom)
 
+
+# 基于物品相似度的推荐引擎
+# standEst()函数，用来计算在给定相似度计算方法的条件下，用户对物品的估计评分值。
+    # standEst()函数的参数包括数据矩阵、用户编号、物品编号和相似度计算方法
+def standEst(dataMat, user, simMeas, item):
+    n = shape(dataMat)[1]
+    simTotal = 0.0
+    ratSimTotal = 0.0
+    for j in range(n):
+        userRating = dataMat[user, j]
+        if userRating == 0:
+            continue
+        # 寻找两个用户都评级的物品
+        overLap = nonzero(logical_and(dataMat[:, item].A>0, dataMat[:, j].A>0))[0]
+        if len(overLap) == 0:similarity =0
+        else: similarity = simMeas(dataMat[overLap,item], \
+                                    dataMat[overLap,j])
+        #print 'the %d and %d similarity is : %f'(iten,j,similarity)
+        simTotal += similarity
+        ratSimTotal += similarity * userRating
+    if simTotal == 0: return 0
+    else: return ratSimTotal/simTotal
+
+
+#recommend()函数，就是推荐引擎，它会调用standEst()函数。 
+def recommend(dataMat, user, N=3, simMeas=cosSim, estMethod=standEst):
+    # 寻找未评级的物品
+    unratedItems = nonzero(dataMat[user, :].A == 0)[1]
+    if len(unratedItems) == 0:
+        return 'you rated everything'
+    itemScores = []
+    for item in unratedItems:
+        estimatedScore = estMethod(dataMat, user, simMeas, item)
+        # 寻找前N个未评级物品
+        itemScores.append((item, estimatedScore))
+        return sorted(itemScores, key=lambda jj: jj[1], reverse=True)[: N]