php|php协同过滤推荐算法【附部分源码】 php|算法

算法核心的公式如下：
【php|php协同过滤推荐算法【附部分源码】】1.余弦相似度（求邻居）：

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2.预测公式（预测a可能会喜欢哪种物品）：

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这里是参考帖子的公式，说实话看这个公式看的有点懵
另外一个比较好解释的帖子请看这里的公式和分析过程：https://www.cnblogs.com/dsgcBlogs/p/8619566.html
看完以上内容开始进入正题【基于用户相似协同过滤推荐】

获取所有用户对物品的关注数矩阵

我们的项目设置多个地方比如：获取物品详情、关注物品等接口调用时进行记录行为，以单位1为一次，可以设置每个行为不同的权重，例如：关注+5、收藏+3等等。hobby_num代表喜爱度

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从数据库查询出构成二维数组并循环生成以下数组（为方便展示，已经转为json格式，php程序内无须转为json，1级键为用户id索引）

{ "1": [ { "user_id": "1", "second_id": "1", "ranking_name": "2018年最受欢迎涂抹式面膜", "hobby_num": "0" }, { "user_id": "1", "second_id": "2", "ranking_name": "2018年最热销猫粮", "hobby_num": "0" }, { "user_id": "1", "second_id": "3", "ranking_name": "2018年最热销的天然猫粮", "hobby_num": "0" }, { "user_id": "1", "second_id": "4", "ranking_name": "2018年性价比高又耐养的宠物", "hobby_num": "0" }, { "user_id": "1", "second_id": "5", "ranking_name": "2018年最长寿的宠物", "hobby_num": "0" }, { "user_id": "1", "second_id": "6", "ranking_name": "2018年最火爆的理科专业", "hobby_num": "0" }, { "user_id": "1", "second_id": "7", "ranking_name": "2018年美国医科学校", "hobby_num": "0" }, { "user_id": "1", "second_id": "8", "ranking_name": "2018年最受女生喜欢的热门专业", "hobby_num": "0" }, { "user_id": "1", "second_id": "9", "ranking_name": "2018年最热门的小语种", "hobby_num": "0" }, { "user_id": "1", "second_id": "10", "ranking_name": "2018年最高人气的英语教育机构", "hobby_num": "0" } ]}

计算余弦相似度cos并从大到小排序【不计算为0的值，0代表没有关注度，$not_hobby代表被推荐用户未观看过的内容列表，也就是hobby_num=0的内容】

//计算分母 $fm = 0; foreach ($data[$user_id] as $item) { if ($item['hobby_num'] != 0) { $fm += $item['hobby_num'] * $item['hobby_num']; } }//根 $fm = sqrt($fm); foreach ($data as $key => $item) { if ($key != $user_id) { //计算分子和分母 $fz = 0; $fm2 = 0; foreach ($item as $i => $j) {//计算分子 if ($data[$user_id][$i]['hobby_num'] != 0 && $j['hobby_num'] != 0) { $fz += $data[$user_id][$i]['hobby_num'] * $j['hobby_num']; }//计算分母2 if ($j['hobby_num'] != 0) { $fm2 += $j['hobby_num'] * $j['hobby_num']; } }$fm2 = sqrt($fm2); if ($fz != 0 && $fm != 0 && $fm2 != 0) { //计算用户的cos值 $cos[$key] = $fz / $fm / $fm2; }} else {//设置用户未看过的内容数组 foreach ($item as $k => $j) { if ($j['hobby_num'] == 0) { $not_hobby[$k] = $j; } }} }//从大到小排序cos值 arsort($cos); //设置好用户对应的cos值 foreach ($cos as $key => $item) { $lingju[] = ['user_id' => $key, 'cos' => $item]; }//取前面3个邻居 $lingju = array_slice($lingju, 0, 3); //设置好对预测推荐的数组 foreach ($lingju as &$i) { foreach ($not_hobby as $k => $j) { $i['hobby'][] = $data[$i['user_id']][$k]; } }//计算predict值 foreach ($not_hobby as $k => &$j) { $predict_fz = 0; $predict_fm = 0; foreach ($lingju as $item) { if ($item['hobby'][$k]['hobby_num'] != 0) { $predict_fz += $item['cos'] * $item['hobby'][$k]['hobby_num']; }$predict_fm += $item['cos']; }$j['predict'] = $predict_fz / sqrt($predict_fm); }//设置排序的第二数组 foreach ($not_hobby as $key => $row) { $volume[$key] = $row['predict']; }//根据predict值排序二维数组 array_multisort($volume, SORT_DESC, $not_hobby);

键为用户id【理论上越接近1则两个用户越相似，此处看你们的机制，因为我们项目的有分领域，并设置测试每个领域有系统设定好的机器人，比如以下1为关注游戏领域的机器人，测试时点击游戏内容则与用户id=1的用户较为相似，cos值为0.42007393952061，可作为邻居】

[ { "user_id": 1, "cos": 0.42007393952061 }, { "user_id": 12, "cos": 0.22096682734453 }, { "user_id": 5, "cos": 0.099333258551861 }, { "user_id": 8, "cos": 0.050731001802594 }, { "user_id": 17, "cos": 0.022953657440244 } ]

[ { "user_id": "389", "second_id": "226", "ranking_name": "100元以内最具性价比的出租屋神器", "hobby_num": "0", "predict": 9.2758507820205 }, { "user_id": "389", "second_id": "46", "ranking_name": "创造101最高人气的成员", "hobby_num": "0", "predict": 6.3466347455929 }, { "user_id": "389", "second_id": "135", "ranking_name": "曾经红极一时的最经典耽美影视作品", "hobby_num": "0", "predict": 2.8248421075833 }, { "user_id": "389", "second_id": "71", "ranking_name": "不论何时回顾，都会感动落泪的动漫情节", "hobby_num": "0", "predict": 2.3112344516591 }, { "user_id": "389", "second_id": "18", "ranking_name": "2018年最让人心动的动漫女主", "hobby_num": "0", "predict": 2.054430623697 }, { "user_id": "389", "second_id": "20", "ranking_name": "日本恐怖漫画", "hobby_num": "0", "predict": 1.7976267957348 }, { "user_id": "389", "second_id": "27", "ranking_name": "亚洲最火爆的动漫", "hobby_num": "0", "predict": 1.7976267957348 }, { "user_id": "389", "second_id": "73", "ranking_name": "国产动画片排行榜", "hobby_num": "0", "predict": 1.7976267957348 }, { "user_id": "389", "second_id": "50", "ranking_name": "2018最佳BL漫画", "hobby_num": "0", "predict": 1.5408229677727 }, { "user_id": "389", "second_id": "19", "ranking_name": "2018年日本耽美漫画", "hobby_num": "0", "predict": 0.77041148388636 }, { "user_id": "389", "second_id": "33", "ranking_name": "80后最爱看的动漫", "hobby_num": "0", "predict": 0.77041148388636 }, { "user_id": "389", "second_id": "74", "ranking_name": "日本最催泪虐心动漫", "hobby_num": "0", "predict": 0.77041148388636 }, { "user_id": "389", "second_id": "221", "ranking_name": "深圳福田区公寓-梅林&景田&车公庙&新洲", "hobby_num": "0", "predict": 0.57721698199284 }, { "user_id": "389", "second_id": "153", "ranking_name": "饭可以不吃但不能不玩的最经典单机游戏", "hobby_num": "0", "predict": 0.46177358559427 }, { "user_id": "389", "second_id": "44", "ranking_name": "中国区-2018年绝地求生最受欢迎的职业战队", "hobby_num": "0", "predict": 0.3463301891957 } ]

此处做法是按照从大到小的预测值排列并推荐。当然了predict值比较小的内容是否推荐，看项目而定，一般predict值小代表和用户不是非常的贴合，也可能不一定是用户喜欢的内容，但一般项目内可能感兴趣的榜单，就推荐几个，一般来说就够了