python實作ItemCF算法

畢設要用到推薦算法，于是今天在在網上查找資料，找到這位仁兄的資料https://blog.csdn.net/Gamer_gyt/article/details/51346159

#!/usr/sbin/env python
# -*- coding:utf-8 -*-

import math
# ItemCF算法
def ItemSimilarity(train):
    # 物品-物品的共同矩陣
    C = dict()
    # 物品被多少個不同使用者購買
    N = dict()
    for u, items in train.items():
        for i in items.keys():
            N.setdefault(i, 0)
            N[i] += 1
            C.setdefault(i, {})
            for j in items.keys():
                if i == j:
                    continue
                C[i].setdefault(j, 0)
                C[i][j] += 1
    # 計算相似度矩陣
    W = dict()
    for i, related_items in C.items():
        W.setdefault(i, {})
        for j, cij in related_items.items():
            W[i][j] = cij / math.sqrt(N[i] * N[j])
    return W

# 推薦前K個使用者
def Recommend(train, user_id, W, K):
    rank = dict()
    action_item = train[user_id]
    for item, score in action_item.items():
        for j, wj in sorted(W[item].items(), key=lambda x:x[1], reverse=True)[0:K]:
            if j in action_item.keys():
                continue
            rank.setdefault(j, 0)
            rank[j] += score * wj
    print rank.items()
    return sorted(rank.items(), key=lambda x:x[1], reverse=True)

train = dict()
for line in open('requests.txt', 'r'):
    user, item, score = line.strip().split(",")
    train.setdefault(user, {})
    train[user][item] = float(score)
W = ItemSimilarity(train)
result = Recommend(train, '5', W, 3)
print result
           

requests.txt

1,101,5.0
1,102,3.0
1,103,2.5
2,101,2.0
2,102,2.5
2,103,5.0
2,104,2.0
3,101,2.0
3,104,4.0
3,105,4.5
3,107,5.0
4,101,5.0
4,103,3.0
4,104,4.5
4,106,4.0
5,101,4.0
5,102,3.0
5,103,2.0
5,104,4.0
5,105,3.5
5,106,4.0
           

将資料整理到excel表中

最後驗證輸出結果為：

分别對1，2，3，4，5使用者進行驗證，發現可以推薦出最适合的，完成。

最後還是感謝這位仁兄了https://blog.csdn.net/Gamer_gyt/article/details/51346159

初次研究算法這方面，有不足之處還請指導。