markov_model.py 文件源码

def top_k_recommendations(self, sequence, k=10, exclude=None, **kwargs):
        if exclude is None:
            exclude = []

        last_item = int(sequence[-1][0])
        if last_item not in self.previous_recommendations:
            self.get_all_recommendations(last_item)

        all_recommendations = deepcopy(self.previous_recommendations[last_item])
        for s in sequence:
            all_recommendations[int(s[0])] = 0
        for i in exclude:
            all_recommendations[i] = 0

        ranking = np.zeros(self.n_items)
        for i, x in enumerate(all_recommendations.most_common(k)):
            ranking[x[0]] = k-i
        return np.argpartition(-ranking, range(k))[:k]