c0f75b3a70cc2e433472de9dfd2406c685b561a9,models/item_ranking/bprmf.py,BPRMF,predict,#BPRMF#Any#Any#,142

Before Change

        saver.save(self.sess, path)

    def predict(self, user_id, item_id):
        return self.sess.run([self.pred_y], feed_dict={self.user_id: user_id, self.item_id: item_id})[0]

    def _get_neg_items(self, data):
        all_items = set(np.arange(self.num_item))
        neg_items = {}

After Change

        user_id = tf.expand_dims(tf.convert_to_tensor(user_id), -1)
        item_id = tf.expand_dims(tf.convert_to_tensor(item_id), -1)
        dummy_neg_id = tf.zeros(item_id.shape, tf.int32)
        pred_y, pred_y_neg = self.model([user_id, item_id, dummy_neg_id])
        return pred_y.numpy()

    def _get_neg_items(self, data):
        all_items = set(np.arange(self.num_item))

Instances