一石二鸟：新闻推荐中召回与排序的统一模型学习
Two Birds with One Stone: Unified Model Learning for Both Recall and Ranking in News Recommendation
摘要：
召回和排序是个性化新闻推荐的两个关键步骤。现有的新闻推荐系统大多采用不同的模型分别进行个性化的新闻召回和排序。然而，维护多个模型导致高计算成本，并提出了很大的挑战，以满足新闻推荐系统的在线延迟要求。为了解决这个问题，本文提出了UniRec，一个统一的方法召回和排名的新闻推荐。在我们的方法中，我们首先使用用户编码器模型从用户的历史新闻点击行为推断用户嵌入排名。然后，我们推导出用户嵌入召回从获得的用户嵌入排名使用它作为注意力查询选择一组基本用户嵌入编码不同的一般用户的兴趣和合成到一个用户嵌入召回。在基准数据集上的大量实验表明，该方法可以提高新闻推荐的召回率和排名效率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
