News recommendation for anonymous readers is a useful but challenging task
for many news portals, where interactions between readers and articles are
limited within a temporary login session. Previous works tend to formulate
session-based recommendation as a next item prediction task, while they neglect
the implicit feedback from user behaviors, which indicates what users really
like or dislike. Hence, we propose a comprehensive framework to model user
behaviors through positive feedback (i.e., the articles they spend more time
on) and negative feedback (i.e., the articles they choose to skip without
clicking in). Moreover, the framework implicitly models the user using their
session start time, and the article using its initial publishing time, in what
we call "neutral feedback". Empirical evaluation on three real-world news
datasets shows the framework's promising performance of more accurate, diverse
and even unexpectedness recommendations than other state-of-the-art
session-based recommendation approaches.

研究了匿名用户的新闻推荐问题，提出了一种基于正负反馈的用户行为建模框架，综合考虑用户和文章的观看时间等不同反馈信息，以实现更精确、多样化和出乎意料的推荐效果。

正面、负面和中性：基于会话的新闻推荐中的隐式反馈建模

Positive, Negative and Neutral: Modeling Implicit Feedback in  Session-based News Recommendation

Recommender systems play a crucial role in mitigating the problem of
information overload by suggesting users' personalized items or services. The
vast majority of traditional recommender systems consider the recommendation
procedure as a static process and make recommendations following a fixed
strategy. In this paper, we propose a novel recommender system with the
capability of continuously improving its strategies during the interactions
with users. We model the sequential interactions between users and a
recommender system as a Markov Decision Process (MDP) and leverage
Reinforcement Learning (RL) to automatically learn the optimal strategies via
recommending trial-and-error items and receiving reinforcements of these items
from users' feedback. Users' feedback can be positive and negative and both
types of feedback have great potentials to boost recommendations. However, the
number of negative feedback is much larger than that of positive one; thus
incorporating them simultaneously is challenging since positive feedback could
be buried by negative one. In this paper, we develop a novel approach to
incorporate them into the proposed deep recommender system (DEERS) framework.
The experimental results based on real-world e-commerce data demonstrate the
effectiveness of the proposed framework. Further experiments have been
conducted to understand the importance of both positive and negative feedback
in recommendations.

文章提出了一种利用强化学习学习交互过程中优化策略的推荐系统，通过深度学习框架将正反馈同时整合到系统中进行优化，证明了该方法可以提高推荐精度。