fossil.py 文件源码

def top_k_recommendations(self, sequence, user_id=None, k=10, exclude=None):
        ''' Recieves a sequence of (id, rating), and produces k recommendations (as a list of ids)
        '''

        if exclude is None:
            exclude = []

        user_items = [i[0] for i in sequence]
        output = self.item_score(user_id, user_items)

        # Put low similarity to viewed items to exclude them from recommendations
        output[[i[0] for i in sequence]] = -np.inf
        output[exclude] = -np.inf

        # find top k according to output
        return list(np.argpartition(-output, range(k))[:k])