要实现推荐功能,可以使用协同过滤算法,以下是一个简单的PHP推荐示例:
$ratings = [
'user1' => [
'item1' => 4,
'item2' => 3,
'item3' => 5,
],
'user2' => [
'item1' => 5,
'item2' => 2,
'item3' => 4,
],
// 其他用户...
];
function similarity($ratings, $item1, $item2) {
$commonUsers = array_intersect_key($ratings[$item1], $ratings[$item2]);
$sum1 = array_sum($ratings[$item1]);
$sum2 = array_sum($ratings[$item2]);
$sum1Sq = array_sum(array_map(function($x) { return pow($x, 2); }, $ratings[$item1]));
$sum2Sq = array_sum(array_map(function($x) { return pow($x, 2); }, $ratings[$item2]));
$pSum = 0;
foreach ($commonUsers as $user => $rating) {
$pSum += $ratings[$item1][$user] * $ratings[$item2][$user];
}
$num = $pSum - ($sum1 * $sum2 / count($commonUsers));
$den = sqrt(($sum1Sq - pow($sum1, 2) / count($commonUsers)) * ($sum2Sq - pow($sum2, 2) / count($commonUsers)));
if ($den == 0) return 0;
return $num / $den;
}
function recommend($ratings, $user) {
$totalScores = [];
$simSums = [];
foreach ($ratings as $otherUser => $items) {
if ($otherUser == $user) continue;
$similarity = similarity($ratings, $user, $otherUser);
if ($similarity <= 0) continue;
foreach ($items as $item => $rating) {
if (!isset($ratings[$user][$item])) {
$totalScores[$item] = 0;
$simSums[$item] = 0;
}
$totalScores[$item] += $rating * $similarity;
$simSums[$item] += $similarity;
}
}
$recommendations = [];
foreach ($totalScores as $item => $score) {
$recommendations[$item] = $score / $simSums[$item];
}
arsort($recommendations);
return $recommendations;
}
使用示例:
$recommendations = recommend($ratings, 'user1');
foreach ($recommendations as $item => $score) {
echo $item . ': ' . $score . "\n";
}
这个示例中,我们假设有两个用户(user1和user2)和三个物品(item1、item2和item3),用户对物品的评分已经给出。然后,我们根据用户的历史评分和物品之间的相似度,为用户推荐其他物品。最后,按照推荐得分进行排序并输出推荐结果。
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