A Hierarchical and Disentangling Interest Learning Framework for Unbiased and True News Recommendation
Authors: 
Shoujin Wang, Wentao Wang, Xiuzhen Zhang, Yan Wang, 
Huan Liu, Fang ChenAuthors Info & Claims
Shoujin Wang, Wentao Wang, Xiuzhen Zhang, Yan Wang, Huan Liu, Fang ChenAuthors Info & ClaimsPages 3200 - 3211
Abstract
In the era of information explosion, news recommender systems are crucial for users to effectively and efficiently discover their interested news. However, most of the existing news recommender systems face two major issues, hampering recommendation quality. Firstly, they often oversimplify users' reading interests, neglecting their hierarchical nature, spanning from high-level event (e.g., US Election) related interests to low-level news article-specifc interests. Secondly, existing work often assumes a simplistic context, disregarding the prevalence of fake news and political bias under the real-world context. This oversight leads to recommendations of biased or fake news, posing risks to individuals and society. To this end, this paper addresses these gaps by introducing a novel framework, the Hierarchical and Disentangling Interest learning framework (HDInt). HDInt incorporates a hierarchical interest learning module and a disentangling interest learning module. The former captures users' high- and low-level interests, enhancing next-news recommendation accuracy. The latter effectively separates polarity and veracity information from news contents and model them more specifcally, promoting fairness- and truth-aware reading interest learning for unbiased and true news recommendations. Extensive experiments on two real-world datasets demonstrate HDInt's superiority over state-of-the-art news recommender systems in delivering accurate, unbiased, and true news recommendations.
Supplemental Material
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Political bias and disinformation often permeate news content, posing a significant threat to social cohesion. Unfortunately, current news recommender engines used by websites and apps typically overlook these issues. They simply suggest news similar to what users have previously read, inadvertently spreading bias and misinformation, especially when users have recently encountered biased or fake news, whether intentionally or unintentionally. This raises a critical challenge: how can we recommend unbiased and truthful news to users, even if they have been exposed to biased or fake content? This challenge has become particularly urgent in the era of generative AI, where creating biased or fake content is becoming increasingly easy and inexpensive. This work is pioneering in its focus on this demanding research problem.
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- A Hierarchical and Disentangling Interest Learning Framework for Unbiased and True News Recommendation
Index terms have been assigned to the content through auto-classification.
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August 2024
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Published: 24 August 2024
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KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
August 25 - 29, 2024
Barcelona, Spain
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