问简单推荐系统

EN

// $for = the user we are getting rec's for
// $user_array = the list of all users
function recommendations($for, $user_array) {

    // For storing the best matching user
    $best_match = array('name'=>'', 'count'=>0);

    foreach ($user_array as $name=>$items) {

        // If this user, skip
        if ($name==$for) continue;

        // Number of matching records
        $c = count(array_intersect($items, $user_array[$for]));

        // If better than the current best match, replace
        if ($c > $best_match['count']) {
            $best_match = array('name'=>$name, 'count'=>$c);
        }
    }

    // If no matches found, return false
    if ($best_match['count']==0)
        return false;

    // Return array of recommendations
    return array_diff($user_array[$best_match['name']], $user_array[$for]);
}

$users = array (
    'john' => array('a', 'b', 'c', 'd', 'e'),
    'alex' => array('a', 'b', 'x', 'y', 'z'),
    'me' => array('a', 'b', 'c', 'f', 'r')
);

print_r(recommendations('me', $users));

DataModel model = new FileDataModel(new File("path/fileName.csv"));

ItemSimilarity similarity = new LogLikelihoodSimilarity(model);

GenericItemBasedRecommender recommender = new GenericItemBasedRecommender(model, similarity);

List<RecommendedItem> recommendations = recommender.recommend(userNumberForRecommendation, numberOfRecommendation);

for(RecommendedItem recommendation:recommendations) {
        System.out.println(recommendation);
}