News recommendation is a core technique used by many online news platforms.
Recommending high-quality news to users is important for keeping good user
experiences and news platforms' reputations. However, existing news
recommendation methods mainly aim to optimize news clicks while ignoring the
quality of news they recommended, which may lead to recommending news with
uninformative content or even clickbaits. In this paper, we propose a
quality-aware news recommendation method named QualityRec that can effectively
improve the quality of recommended news. In our approach, we first propose an
effective news quality evaluation method based on the distributions of users'
reading dwell time on news. Next, we propose to incorporate news quality
information into user interest modeling by designing a content-quality
attention network to select clicked news based on both news semantics and
qualities. We further train the recommendation model with an auxiliary news
quality prediction task to learn quality-aware recommendation model, and we add
a recommendation quality regularization loss to encourage the model to
recommend higher-quality news. Extensive experiments on two real-world datasets
show that QualityRec can effectively improve the overall quality of recommended
news and reduce the recommendation of low-quality news, with even slightly
better recommendation accuracy.

本文提出了一种可以有效提高推荐新闻的质量的质量感知型新闻推荐方法 QualityRec，采用基于用户阅读停留时间分布的新闻质量评估方法，并设计内容质量注意力网络将新闻质量信息融入用户兴趣建模，训练辅助的新闻质量预测任务来学习质量感知型的推荐模型，并添加推荐质量正则化的损失来鼓励模型推荐更高质量的新闻，通过在两个真实数据集上进行的实验表明 QualityRec 可以有效提高推荐新闻的整体质量，并减少对低质量新闻的推荐。

质量感知新闻推荐

Quality-aware News Recommendation

User interest modeling is critical for personalized news recommendation.
Existing news recommendation methods usually learn a single user embedding for
each user from their previous behaviors to represent their overall interest.
However, user interest is usually diverse and multi-grained, which is difficult
to be accurately modeled by a single user embedding. In this paper, we propose
a news recommendation method with hierarchical user interest modeling, named
HieRec. Instead of a single user embedding, in our method each user is
represented in a hierarchical interest tree to better capture their diverse and
multi-grained interest in news. We use a three-level hierarchy to represent 1)
overall user interest; 2) user interest in coarse-grained topics like sports;
and 3) user interest in fine-grained topics like football. Moreover, we propose
a hierarchical user interest matching framework to match candidate news with
different levels of user interest for more accurate user interest targeting.
Extensive experiments on two real-world datasets validate our method can
effectively improve the performance of user modeling for personalized news
recommendation.

该研究提出了 HieRec，一种基于层次化用户兴趣模型的新闻推荐方法，使用三级层次结构表示用户的兴趣，并使用层次匹配框架实现更准确的用户兴趣目标定位，实验结果表明该方法能够有效改善个性化新闻推荐的用户建模效果。

HieRec: 面向个性化新闻推荐的层次化用户兴趣建模

HieRec: Hierarchical User Interest Modeling for Personalized News  Recommendation

Personalized news recommendation methods are widely used in online news
services. These methods usually recommend news based on the matching between
news content and user interest inferred from historical behaviors. However,
these methods usually have difficulties in making accurate recommendations to
cold-start users, and tend to recommend similar news with those users have
read. In general, popular news usually contain important information and can
attract users with different interests. Besides, they are usually diverse in
content and topic. Thus, in this paper we propose to incorporate news
popularity information to alleviate the cold-start and diversity problems for
personalized news recommendation. In our method, the ranking score for
recommending a candidate news to a target user is the combination of a
personalized matching score and a news popularity score. The former is used to
capture the personalized user interest in news. The latter is used to measure
time-aware popularity of candidate news, which is predicted based on news
content, recency, and real-time CTR using a unified framework. Besides, we
propose a popularity-aware user encoder to eliminate the popularity bias in
user behaviors for accurate interest modeling. Experiments on two real-world
datasets show our method can effectively improve the accuracy and diversity for
news recommendation.

本文提出了一种个性化新闻推荐方法，它通过将新闻的热门信息和用户兴趣结合，从而解决了针对新用户和兴趣多样化的问题，提高了推荐的准确性和多样性。