diff --git a/README.md b/README.md
index 699aed8..3fbc040 100644
--- a/README.md
+++ b/README.md
@@ -46,9 +46,15 @@ http://blog.csdn.net/gamer_gyt/article/details/51346159<br/>
 13：基于随机变量的熵来进行数据建模和分析<br/>
 http://blog.csdn.net/gamer_gyt/article/details/53729868<br/>
 
-14：推荐算法的回顾总结<br/>
+14：【推荐系统】推荐算法的回顾总结<br/>
 http://blog.csdn.net/gamer_gyt/article/details/74367714<br/>
 
+15：【推荐系统】基于协同的Slope One算法原理介绍和实现<br/>
+https://blog.csdn.net/Gamer_gyt/article/details/98210975
+
+16：【推荐系统】基于三部图网络结构的知识推荐算法<br/>
+https://blog.csdn.net/Gamer_gyt/article/details/98508579
+
 --------------------------
 # 数据采集样例代码
 文件夹为 spider，其内会逐步增加一些爬虫程序和数据，希望对你们有帮助
diff --git a/Recommend/SlopeOne.py b/Recommend/SlopeOne.py
new file mode 100644
index 0000000..54dfa5a
--- /dev/null
+++ b/Recommend/SlopeOne.py
@@ -0,0 +1,71 @@
+# -*- coding: utf-8 -*-
+
+"""
+    Time    : 2019-08-02 15:29
+    Author  : Thinkgamer
+    File    : SlopeOne.py
+    Software: PyCharm
+"""
+
+class SlopeOne:
+    def __init__(self):
+        self.user_rate, self.item_rate = self.loadData()
+
+    # 加载数据
+    def loadData(self):
+        user_rate = {
+            "U1": {"a": 2, "b": 3, "c": 3, "d": 4},
+            "U2": {"b": 4, "c": 2, "d": 3, "e": 3},
+            "U3": {"a": 4, "b": 2, "c": 3, "e": 2},
+            "U4": {"a": 3, "c": 5, "d": 4, "e": 3}
+        }
+        item_rate = {
+            "a": {"U1": 2, "U3": 4, "U4": 3},
+            "b": {"U1": 3, "U2": 4, "U3": 2},
+            "c": {"U1": 3, "U2": 2, "U3": 3, "U4": 5},
+            "d": {"U1": 4, "U2": 3, "U4": 4},
+            "e": {"U2": 3, "U3": 2, "U4": 3}
+        }
+        return user_rate,item_rate
+
+    # 计算物品之间的评分偏差
+    def cal_item_avg_diff(self):
+        avgs_dict = {}
+        for item1 in self.item_rate.keys():
+            for item2 in self.item_rate.keys():
+                avg = 0.0
+                user_count = 0
+                if item1 != item2:
+                    for user in self.user_rate.keys():
+                        user_rate = self.user_rate[user]
+                        if item1 in user_rate.keys() and item2 in user_rate.keys():
+                            user_count += 1
+                            avg += user_rate[item1] - user_rate[item2]
+                    avg = avg / user_count
+                avgs_dict.setdefault(item1,{})
+                avgs_dict[item1][item2] = avg
+        return avgs_dict
+
+    # 计算两个电影的共同评分人数
+    def item_both_rate_user(self, item1, item2):
+        count = 0
+        for user in self.user_rate.keys():
+            if item1 in self.user_rate[user].keys() and item2 in self.user_rate[user].keys():
+                count += 1
+        return count
+
+    # 预估评分
+    def predict(self, user, item, avgs_dict):
+        total = 0.0 # 分子
+        count = 0   # 分母
+        for item1 in self.user_rate[user].keys():
+            num = self.item_both_rate_user(item, item1)
+            count += num
+            total += num * (self.user_rate[user][item1] - avgs_dict[item][item1])
+        return total/count
+
+if __name__ == "__main__":
+    slope = SlopeOne()
+    avgs_dict = slope.cal_item_avg_diff()
+    result = slope.predict("U2", "a", avgs_dict)
+    print("U2 对 a的预测评分为: %s" % result)
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