def METHOD_NAME(golden_file, predict_file):
    # Read ground-truth
    reference = read_reference(golden_file)
    # Read predictions
    k = 10
    predictions = read_submission(predict_file, reference, k)
    # Compute score for each text
    r1_stat, r5_stat, r10_stat = 0, 0, 0
    for qid in reference.keys():
        ground_truth_ids = set(reference[qid])
        top10_pred_ids = predictions[qid]
        if any([idx in top10_pred_ids[:1] for idx in ground_truth_ids]):
            r1_stat += 1
        if any([idx in top10_pred_ids[:5] for idx in ground_truth_ids]):
            r5_stat += 1
        if any([idx in top10_pred_ids[:10] for idx in ground_truth_ids]):
            r10_stat += 1
    # The higher score, the better
    r1, r5, r10 = r1_stat * 1.0 / len(reference), r5_stat * 1.0 / len(reference), r10_stat * 1.0 / len(reference)
    mean_recall = (r1 + r5 + r10) / 3.0
    result = [mean_recall, r1, r5, r10]
    result = [score * 100 for score in result]
    return result