  Personalization of natural language generation plays a vital role in a large
spectrum of tasks, such as explainable recommendation, review summarization and
dialog systems. In these tasks, user and item IDs are important identifiers for
personalization. Transformer, which is demonstrated with strong language
modeling capability, however, is not personalized and fails to make use of the
user and item IDs since the ID tokens are not even in the same semantic space
as the words. To address this problem, we present a PErsonalized Transformer
for Explainable Recommendation (PETER), on which we design a simple and
effective learning objective that utilizes the IDs to predict the words in the
target explanation, so as to endow the IDs with linguistic meanings and to
achieve personalized Transformer. Besides generating explanations, PETER can
also make recommendations, which makes it a unified model for the whole
recommendation-explanation pipeline. Extensive experiments show that our small
unpretrained model outperforms fine-tuned BERT on the generation task, in terms
of both effectiveness and efficiency, which highlights the importance and the
nice utility of our design.
