Two Birds with One Stone: Unified Model Learning for Both Recall and Ranking in News Recommendation (2022)

Abstract: 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 challenges 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.

## introduction news recommendation techniques are widely used by many online news websites and apps to provide personalized news services @xcite . recall and ranking are two critical steps in personalized news recommender systems @xcite . as shown in fig. 1 , when a user visits a news platform, the recommender system first recalls a set of candidate news from a large-scale news pool, and then ranks candidate news for personalized news display @xcite . both news recall and ranking have been widely studied @xcite @xcite @xcite @xcite . in online news recommender systems, recall and ranking are * corresponding author. usually conducted separately with different models, as shown in fig. 1 . however, maintaining separate models for news recall and ranking in large-scale news recommender systems usually leads to heavy computation and memory cost @xcite , and it may be difficult to meet the latency requirement of online news services. learning a unified model for personalized news recall and ranking would be greatly beneficial for alleviating the computation load of news recommender systems. however, it is a non-trivial task because the goals of recall and ranking are not the same @xcite . ranking usually aims to accurately rank candidates based on their relevance to user interests @xcite @xcite , while recall mainly aims to form a candidate pool that can comprehensively cover user interests @xcite . thus, the model needs to adapt to the different goals of recall and ranking without hurting their performance. in this paper, we propose a news recommendation method named unirec, which can learn a unified user model for personalized news recall and ranking. in our method, we first encode news into embeddings with a news encoder, and learn a user embedding for ranking from the embeddings of historical clicked news. we further derive the user embedding for recall by using the user embedding for ranking as the attention query to select a news encoder news encoder news encoder user encoder news pool historical clicked news … … … 𝒖𝒖 𝑟𝑟𝑟𝑟 𝒖𝒖 𝑟𝑟𝑟𝑟 𝒓𝒓 1 𝒓𝒓 2 𝒓𝒓 𝑁𝑁 𝐷𝐷 1 𝐷𝐷 2 𝐷𝐷 𝑁𝑁 𝐷𝐷 𝑇𝑇 𝑐𝑐 user embedding for ranking user embedding for recall basis user embedding memory candidate list set of basis user embeddings that encode different general user interest aspects and synthesize them into a user embedding for recall. in the test phase, we only use the basis user embeddings with top attention weights to compose the user embedding for recall to filter noisy user interests. extensive experiments on a real-world dataset demonstrate that our method can conduct personalized news recall and ranking with a unified model and meanwhile achieve promising recall and ranking performance. ## methodology the overall framework of unirec is shown in fig. 2 . we first learn a user embedding for ranking from the user's historical clicked news. we then derive a user embedding for recall from the user embedding for ranking and a set of basis user embeddings that encode different general interests. their details are introduced as follows. ## conclusion in this paper, we present a unified approach for recall and ranking in news recommendation. in our method, we first infer a user embedding for ranking from historical news click behaviors via a user encoder model. then we derive a user embedding for recall from the obtained user embedding for ranking by regarding it as attention query to select a set of basis user embeddings that encode different general user interests. extensive experiments on a benchmark dataset validate the effectiveness of our approach in both news ranking and recall.