fpmc.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 = []

        last_item = sequence[-1][0]
        output = np.dot(self.V_user_item[user_id, :], self.V_item_user.T) + np.dot(self.V_prev_next[last_item, :], self.V_next_prev.T)

        # 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])