<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def recommend(self, userid, user_items, N=10, filter_already_liked_items=True, filter_items=None, recalculate_user=False):
 """ returns the best N recommendations for a user given its id"""

        if userid >= user_items.shape[0]:
            raise ValueError("userid is out of bounds of the user_items matrix")

        # recalculate_user is ignored because this is not a model based algorithm
        items = N
        if filter_items:
            items += len(filter_items)

        indices, data = self.scorer.recommend(userid, user_items.indptr, user_items.indices,
                                              user_items.data, K=items,
                                              remove_own_likes=filter_already_liked_items)
        best = sorted(zip(indices, data), key=lambda x: -x[1])

        if not filter_items:
            return best

        liked = set(filter_items)
        return list(itertools.islice((rec for rec in best if rec[0] not in liked), N))