python">{"cells": [{"cell_type": "markdown","metadata": {},"source": ["# 基于用户的协同过滤算法"]},{"cell_type": "code","execution_count": 1,"metadata": {},"outputs": [],"source": ["# 导入包\n","import random\n","import math\n","import time\n","from tqdm import tqdm"]},{"cell_type": "markdown","metadata": {},"source": ["## 一. 通用函数定义"]},{"cell_type": "code","execution_count": 2,"metadata": {},"outputs": [],"source": ["# 定义装饰器，监控运行时间\n","def timmer(func):\n","    def wrapper(*args, **kwargs):\n","        start_time = time.time()\n","        res = func(*args, **kwargs)\n","        stop_time = time.time()\n","        print('Func %s, run time: %s' % (func.__name__, stop_time - start_time))\n","        return res\n","    return wrapper"]},{"cell_type": "markdown","metadata": {},"source": ["### 1. 数据处理相关\n","1. load data\n","2. split data"]},{"cell_type": "code","execution_count": 3,"metadata": {},"outputs": [],"source": ["class Dataset():\n","    \n","    def __init__(self, fp):\n","        # fp: data file path\n","        self.data = self.loadData(fp)\n","    \n","    @timmer\n","    def loadData(self, fp):\n","        data = []\n","        for l in open(fp):\n","            data.append(tuple(map(int, l.strip().split('::')[:2])))\n","        return data\n","    \n","    @timmer\n","    def splitData(self, M, k, seed=1):\n","        '''\n","        :params: data, 加载的所有(user, item)数据条目\n","        :params: M, 划分的数目，最后需要取M折的平均\n","        :params: k, 本次是第几次划分，k~[0, M)\n","        :params: seed, random的种子数，对于不同的k应设置成一样的\n","        :return: train, test\n","        '''\n","        train, test = [], []\n","        random.seed(seed)\n","        for user, item in self.data:\n","            # 这里与书中的不一致，本人认为取M-1较为合理，因randint是左右都覆盖的\n","            if random.randint(0, M-1) == k:  \n","                test.append((user, item))\n","            else:\n","                train.append((user, item))\n","\n","        # 处理成字典的形式，user->set(items)\n","        def convert_dict(data):\n","            data_dict = {}\n","            for user, item in data:\n","                if user not in data_dict:\n","                    data_dict[user] = set()\n","                data_dict[user].add(item)\n","            data_dict = {k: list(data_dict[k]) for k in data_dict}\n","            return data_dict\n","\n","        return convert_dict(train), convert_dict(test)"]},{"cell_type": "markdown","metadata": {},"source": ["### 2. 评价指标\n","1. Precision\n","2. Recall\n","3. Coverage\n","4. Popularity(Novelty)"]},{"cell_type": "code","execution_count": 4,"metadata": {},"outputs": [],"source": ["class Metric():\n","    \n","    def __init__(self, train, test, GetRecommendation):\n","        '''\n","        :params: train, 训练数据\n","        :params: test, 测试数据\n","        :params: GetRecommendation, 为某个用户获取推荐物品的接口函数\n","        '''\n","        self.train = train\n","        self.test = test\n","        self.GetRecommendation = GetRecommendation\n","        self.recs = self.getRec()\n","        \n","    # 为test中的每个用户进行推荐\n","    def getRec(self):\n","        recs = {}\n","        for user in self.test:\n","            rank = self.GetRecommendation(user)\n","            recs[user] = rank\n","        return recs\n","        \n","    # 定义精确率指标计算方式\n","    def precision(self):\n","        all, hit = 0, 0\n","        for user in self.test:\n","            test_items = set(self.test[user])\n","            rank = self.recs[user]\n","            for item, score in rank:\n","                if item in test_items:\n","                    hit += 1\n","            all += len(rank)\n","        return round(hit / all * 100, 2)\n","    \n","    # 定义召回率指标计算方式\n","    def recall(self):\n","        all, hit = 0, 0\n","        for user in self.test:\n","            test_items = set(self.test[user])\n","            rank = self.recs[user]\n","            for item, score in rank:\n","                if item in test_items:\n","                    hit += 1\n","            all += len(test_items)\n","        return round(hit / all * 100, 2)\n","    \n","    # 定义覆盖率指标计算方式\n","    def coverage(self):\n","        all_item, recom_item = set(), set()\n","        for user in self.test:\n","            for item in self.train[user]:\n","                all_item.add(item)\n","            rank = self.recs[user]\n","            for item, score in rank:\n","                recom_item.add(item)\n","        return round(len(recom_item) / len(all_item) * 100, 2)\n","    \n","    # 定义新颖度指标计算方式\n","    def popularity(self):\n","        # 计算物品的流行度\n","        item_pop = {}\n","        for user in self.train:\n","            for item in self.train[user]:\n","                if item not in item_pop:\n","                    item_pop[item] = 0\n","                item_pop[item] += 1\n","\n","        num, pop = 0, 0\n","        for user in self.test:\n","            rank = self.recs[user]\n","            for item, score in rank:\n","                # 取对数，防止因长尾问题带来的被流行物品所主导\n","                pop += math.log(1 + item_pop[item])\n","                num += 1\n","        return round(pop / num, 6)\n","    \n","    def eval(self):\n","        metric = {'Precision': self.precision(),\n","                  'Recall': self.recall(),\n","                  'Coverage': self.coverage(),\n","                  'Popularity': self.popularity()}\n","        print('Metric:', metric)\n","        return metric"]},{"cell_type": "markdown","metadata": {},"source": ["## 二. 算法实现\n","1. Random\n","2. MostPopular\n","3. UserCF\n","4. UserIIF"]},{"cell_type": "code","execution_count": 5,"metadata": {},"outputs": [],"source": ["# 1. 随机推荐\n","def Random(train, K, N):\n","    '''\n","    :params: train, 训练数据集\n","    :params: K, 可忽略\n","    :params: N, 超参数，设置取TopN推荐物品数目\n","    :return: GetRecommendation，推荐接口函数\n","    '''\n","    items = {}\n","    for user in train:\n","        for item in train[user]:\n","            items[item] = 1\n","    \n","    def GetRecommendation(user):\n","        # 随机推荐N个未见过的\n","        user_items = set(train[user])\n","        rec_items = {k: items[k] for k in items if k not in user_items}\n","        rec_items = list(rec_items.items())\n","        random.shuffle(rec_items)\n","        return rec_items[:N]\n","    \n","    return GetRecommendation"]},{"cell_type": "code","execution_count": 6,"metadata": {},"outputs": [],"source": ["# 2. 热门推荐\n","def MostPopular(train, K, N):\n","    '''\n","    :params: train, 训练数据集\n","    :params: K, 可忽略\n","    :params: N, 超参数，设置取TopN推荐物品数目\n","    :return: GetRecommendation, 推荐接口函数\n","    '''\n","    items = {}\n","    for user in train:\n","        for item in train[user]:\n","            if item not in items:\n","                items[item] = 0\n","            items[item] += 1\n","        \n","    def GetRecommendation(user):\n","        # 随机推荐N个没见过的最热门的\n","        user_items = set(train[user])\n","        rec_items = {k: items[k] for k in items if k not in user_items}\n","        rec_items = list(sorted(rec_items.items(), key=lambda x: x[1], reverse=True))\n","        return rec_items[:N]\n","    \n","    return GetRecommendation"]},{"cell_type": "code","execution_count": 7,"metadata": {},"outputs": [],"source": ["# 3. 基于用户余弦相似度的推荐\n","def UserCF(train, K, N):\n","    '''\n","    :params: train, 训练数据集\n","    :params: K, 超参数，设置取TopK相似用户数目\n","    :params: N, 超参数，设置取TopN推荐物品数目\n","    :return: GetRecommendation, 推荐接口函数\n","    '''\n","    # 计算item->user的倒排索引\n","    item_users = {}\n","    for user in train:\n","        for item in train[user]:\n","            if item not in item_users:\n","                item_users[item] = []\n","            item_users[item].append(user)\n","    \n","    # 计算用户相似度矩阵\n","    sim = {}\n","    num = {}\n","    for item in item_users:\n","        users = item_users[item]\n","        for i in range(len(users)):\n","            u = users[i]\n","            if u not in num:\n","                num[u] = 0\n","            num[u] += 1\n","            if u not in sim:\n","                sim[u] = {}\n","            for j in range(len(users)):\n","                if j == i: continue\n","                v = users[j]\n","                if v not in sim[u]:\n","                    sim[u][v] = 0\n","                sim[u][v] += 1\n","    for u in sim:\n","        for v in sim[u]:\n","            sim[u][v] /= math.sqrt(num[u] * num[v])\n","    \n","    # 按照相似度排序\n","    sorted_user_sim = {k: list(sorted(v.items(), \\\n","                               key=lambda x: x[1], reverse=True)) \\\n","                       for k, v in sim.items()}\n","    \n","    # 获取接口函数\n","    def GetRecommendation(user):\n","        items = {}\n","        seen_items = set(train[user])\n","        for u, _ in sorted_user_sim[user][:K]:\n","            for item in train[u]:\n","                # 要去掉用户见过的\n","                if item not in seen_items:\n","                    if item not in items:\n","                        items[item] = 0\n","                    items[item] += sim[user][u]\n","        recs = list(sorted(items.items(), key=lambda x: x[1], reverse=True))[:N]\n","        return recs\n","    \n","    return GetRecommendation"]},{"cell_type": "code","execution_count": 8,"metadata": {},"outputs": [],"source": ["# 4. 基于改进的用户余弦相似度的推荐\n","def UserIIF(train, K, N):\n","    '''\n","    :params: train, 训练数据集\n","    :params: K, 超参数，设置取TopK相似用户数目\n","    :params: N, 超参数，设置取TopN推荐物品数目\n","    :return: GetRecommendation, 推荐接口函数\n","    '''\n","    # 计算item->user的倒排索引\n","    item_users = {}\n","    for user in train:\n","        for item in train[user]:\n","            if item not in item_users:\n","                item_users[item] = []\n","            item_users[item].append(user)\n","    \n","    # 计算用户相似度矩阵\n","    sim = {}\n","    num = {}\n","    for item in item_users:\n","        users = item_users[item]\n","        for i in range(len(users)):\n","            u = users[i]\n","            if u not in num:\n","                num[u] = 0\n","            num[u] += 1\n","            if u not in sim:\n","                sim[u] = {}\n","            for j in range(len(users)):\n","                if j == i: continue\n","                v = users[j]\n","                if v not in sim[u]:\n","                    sim[u][v] = 0\n","                # 相比UserCF，主要是改进了这里\n","                sim[u][v] += 1 / math.log(1 + len(users))\n","    for u in sim:\n","        for v in sim[u]:\n","            sim[u][v] /&#