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php推荐算法实现

2026-02-15 23:55:29PHP

PHP 推荐算法实现

推荐算法在电商、社交网络和内容平台中广泛应用。PHP 可以通过多种方式实现推荐算法，以下是一些常见的方法和示例代码。

基于内容的推荐

基于内容的推荐通过分析用户历史行为或项目特征生成推荐。例如，根据用户浏览的商品标签推荐相似商品。

function contentBasedRecommendation($userPreferences, $items) {
    $recommendations = [];
    foreach ($items as $itemId => $itemTags) {
        $similarity = 0;
        foreach ($userPreferences as $tag => $weight) {
            if (isset($itemTags[$tag])) {
                $similarity += $weight * $itemTags[$tag];
            }
        }
        if ($similarity > 0) {
            $recommendations[$itemId] = $similarity;
        }
    }
    arsort($recommendations);
    return array_keys($recommendations);
}

协同过滤推荐

协同过滤分为用户协同过滤和物品协同过滤。用户协同过滤通过相似用户的行为推荐物品。

function userBasedCollaborativeFiltering($userId, $userItemMatrix, $k = 5) {
    $similarUsers = [];
    foreach ($userItemMatrix as $otherUserId => $items) {
        if ($otherUserId == $userId) continue;
        $similarity = pearsonCorrelation($userItemMatrix[$userId], $items);
        $similarUsers[$otherUserId] = $similarity;
    }
    arsort($similarUsers);
    $topSimilarUsers = array_slice($similarUsers, 0, $k, true);
    $recommendations = [];
    foreach ($topSimilarUsers as $similarUserId => $similarity) {
        foreach ($userItemMatrix[$similarUserId] as $itemId => $rating) {
            if (!isset($userItemMatrix[$userId][$itemId])) {
                $recommendations[$itemId] = ($recommendations[$itemId] ?? 0) + $similarity * $rating;
            }
        }
    }
    arsort($recommendations);
    return array_keys($recommendations);
}

矩阵分解推荐

矩阵分解通过降维技术提取用户和物品的隐含特征，常用于处理稀疏矩阵。

function matrixFactorization($userItemMatrix, $k = 10, $epochs = 100, $alpha = 0.01, $lambda = 0.02) {
    $nUsers = count($userItemMatrix);
    $nItems = count($userItemMatrix[0]);
    $P = array_fill(0, $nUsers, array_fill(0, $k, rand() / getrandmax()));
    $Q = array_fill(0, $nItems, array_fill(0, $k, rand() / getrandmax()));
    for ($epoch = 0; $epoch < $epochs; $epoch++) {
        foreach ($userItemMatrix as $userId => $items) {
            foreach ($items as $itemId => $rating) {
                $error = $rating - dotProduct($P[$userId], $Q[$itemId]);
                for ($i = 0; $i < $k; $i++) {
                    $P[$userId][$i] += $alpha * (2 * $error * $Q[$itemId][$i] - $lambda * $P[$userId][$i]);
                    $Q[$itemId][$i] += $alpha * (2 * $error * $P[$userId][$i] - $lambda * $Q[$itemId][$i]);
                }
            }
        }
    }
    return ['P' => $P, 'Q' => $Q];
}

混合推荐系统

混合推荐结合多种推荐算法以提高准确性和覆盖率。例如，结合基于内容和协同过滤的结果。

function hybridRecommendation($userId, $userPreferences, $userItemMatrix, $items) {
    $contentBased = contentBasedRecommendation($userPreferences, $items);
    $collaborativeBased = userBasedCollaborativeFiltering($userId, $userItemMatrix);
    $recommendations = array_merge($contentBased, $collaborativeBased);
    $recommendations = array_unique($recommendations);
    return $recommendations;
}

辅助函数

以下是一些辅助函数的实现，用于计算相似度和点积。

function pearsonCorrelation($vector1, $vector2) {
    $n = count($vector1);
    $sum1 = array_sum($vector1);
    $sum2 = array_sum($vector2);
    $sum1Sq = array_sum(array_map(function($x) { return $x * $x; }, $vector1));
    $sum2Sq = array_sum(array_map(function($x) { return $x * $x; }, $vector2));
    $pSum = 0;
    foreach ($vector1 as $key => $value) {
        if (isset($vector2[$key])) {
            $pSum += $value * $vector2[$key];
        }
    }
    $num = $pSum - ($sum1 * $sum2 / $n);
    $den = sqrt(($sum1Sq - ($sum1 * $sum1 / $n)) * ($sum2Sq - ($sum2 * $sum2 / $n)));
    return $den == 0 ? 0 : $num / $den;
}

function dotProduct($vector1, $vector2) {
    $product = 0;
    for ($i = 0; $i < count($vector1); $i++) {
        $product += $vector1[$i] * $vector2[$i];
    }
    return $product;
}