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PHP实现推荐

2026-02-14 06:58:05PHP

PHP 推荐系统实现方法

基于内容的推荐

内容推荐通过分析用户历史行为和项目特征匹配相似内容。在PHP中可使用TF-IDF算法计算文本相似度。

// 计算TF-IDF
function tfidf($terms, $documents) {
    $tf = array_count_values($terms);
    array_walk($tf, function(&$value, $key) use ($terms) {
        $value = $value / count($terms);
    });

    $idf = [];
    $total_docs = count($documents);
    foreach(array_unique($terms) as $term) {
        $docs_with_term = 0;
        foreach($documents as $doc) {
            if(in_array($term, $doc)) $docs_with_term++;
        }
        $idf[$term] = log($total_docs / ($docs_with_term + 1));
    }

    $tfidf = [];
    foreach($tf as $term => $value) {
        $tfidf[$term] = $value * $idf[$term];
    }
    return $tfidf;
}

协同过滤推荐

协同过滤分为用户协同和项目协同两种方式，通过PHP数组实现用户评分矩阵：

$userRatings = [
    'User1' => ['ItemA' => 5, 'ItemB' => 3],
    'User2' => ['ItemA' => 4, 'ItemC' => 2]
];

// 计算余弦相似度
function cosineSimilarity($vec1, $vec2) {
    $dotProduct = $magnitude1 = $magnitude2 = 0;
    $keys = array_unique(array_merge(array_keys($vec1), array_keys($vec2)));

    foreach($keys as $key) {
        $v1 = $vec1[$key] ?? 0;
        $v2 = $vec2[$key] ?? 0;
        $dotProduct += $v1 * $v2;
        $magnitude1 += $v1 * $v1;
        $magnitude2 += $v2 * $v2;
    }

    return $dotProduct / (sqrt($magnitude1) * sqrt($magnitude2));
}

混合推荐系统

结合内容推荐和协同过滤的优势：

class HybridRecommender {
    private $contentWeights;
    private $cfWeights;

    public function __construct($contentWeight = 0.5, $cfWeight = 0.5) {
        $this->contentWeights = $contentWeight;
        $this->cfWeights = $cfWeight;
    }

    public function recommend($userId) {
        $contentScores = $this->getContentScores($userId);
        $cfScores = $this->getCFScores($userId);

        $combined = [];
        foreach($contentScores as $item => $score) {
            $combined[$item] = $score * $this->contentWeights 
                + ($cfScores[$item] ?? 0) * $this->cfWeights;
        }

        arsort($combined);
        return array_slice($combined, 0, 5, true);
    }
}

数据库设计

推荐系统通常需要以下基本表结构：

CREATE TABLE users (
    id INT PRIMARY KEY,
    username VARCHAR(50)
);

CREATE TABLE items (
    id INT PRIMARY KEY,
    title VARCHAR(100),
    description TEXT
);

CREATE TABLE user_actions (
    user_id INT,
    item_id INT,
    action_type ENUM('view','purchase','rating'),
    value FLOAT,
    timestamp DATETIME,
    FOREIGN KEY (user_id) REFERENCES users(id),
    FOREIGN KEY (item_id) REFERENCES items(id)
);

性能优化建议

使用Redis缓存热门推荐结果：

$redis = new Redis();
$redis->connect('127.0.0.1', 6379);

$cacheKey = "recs:$userId";
if(!$redis->exists($cacheKey)) {
    $recommendations = $recommender->generate($userId);
    $redis->setex($cacheKey, 3600, json_encode($recommendations));
}
return json_decode($redis->get($cacheKey), true);

实现推荐系统时需要考虑冷启动问题，可通过热门内容填充、用户注册信息收集等方式解决。对于大规模数据，建议使用专门的推荐引擎如Mahout或TensorFlow Serving，PHP通过API调用获取推荐结果。

PHP实现推荐