  Recall and ranking are two critical steps in personalized news
recommendation. Most existing news recommender systems conduct personalized
news recall and ranking separately with different models. However, maintaining
multiple models leads to high computational cost and poses great challenge to
meeting the online latency requirement of news recommender systems. In order to
handle this problem, in this paper we propose UniRec, a unified method for
recall and ranking in news recommendation. In our method, we first infer user
embedding for ranking from the historical news click behaviors of a user using
a user encoder model. Then we derive the user embedding for recall from the
obtained user embedding for ranking by using it as the attention query to
select a set of basis user embeddings which encode different general user
interests and synthesize them into a user embedding for recall. The extensive
experiments on benchmark dataset demonstrate that our method can improve both
efficiency and effectiveness for recall and ranking in news recommendation.
