2 research outputs found
DebiasGAN: Eliminating Position Bias in News Recommendation with Adversarial Learning
News recommendation is important for improving news reading experience of
users. Users' news click behaviors are widely used for inferring user interests
and predicting future clicks. However, click behaviors are heavily affected by
the biases brought by the positions of news displayed on the webpage. It is
important to eliminate the effect of position biases on the recommendation
model to accurately target user interests. In this paper, we propose a news
recommendation method named DebiasGAN that can effectively eliminate the effect
of position biases via adversarial learning. We use a bias-aware click model to
capture the influence of position bias on click behaviors, and we use a
bias-invariant click model with random candidate news positions to estimate the
ideally unbiased click scores. We apply adversarial learning techniques to the
hidden representations learned by the two models to help the bias-invariant
click model capture the bias-independent interest of users on news.
Experimental results on two real-world datasets show that DebiasGAN can
effectively improve the accuracy of news recommendation by eliminating position
biases
Personalized News Recommendation: A Survey
Personalized news recommendation is an important technique to help users find
their interested news information and alleviate their information overload. It
has been extensively studied over decades and has achieved notable success in
improving users' news reading experience. However, there are still many
unsolved problems and challenges that need to be further studied. To help
researchers master the advances in personalized news recommendation over the
past years, in this paper we present a comprehensive overview of personalized
news recommendation. Instead of following the conventional taxonomy of news
recommendation methods, in this paper we propose a novel perspective to
understand personalized news recommendation based on its core problems and the
associated techniques and challenges. We first review the techniques for
tackling each core problem in a personalized news recommender system and the
challenges they face. Next, we introduce the public datasets and evaluation
methods for personalized news recommendation. We then discuss the key points on
improving the responsibility of personalized news recommender systems. Finally,
we raise several research directions that are worth investigating in the
future. This paper can provide up-to-date and comprehensive views to help
readers understand the personalized news recommendation field. We hope this
paper can facilitate research on personalized news recommendation and as well
as related fields in natural language processing and data mining