What is it about?
News recommenders predict a user's interest in a news item, typically based on their reading history. The currently most successful algorithms for this purpose are deep learning based, and are difficult to interpret. We do not know for which reasons such a recommender proposes a certain news item to a user. In this work, we found a way to open this black box. Our method reveals which news in a user's reading history have an influence on a particular recommendation. This way, we can check what the system learned and whether it actually attends to reasonable parts of reading histories to compute its recommendations.
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Why is it important?
News recommender systems are deployed in many commercial applications. They can help us handle the large quantity of incoming news every day and point out the ones that are actually relevant to us. At the same time news consumption is an important factor in our opinion formation and recommenders may affect it. A blackbox system may not function correctly, point out the wrong news or even bias its users. Therefore, it is very important to get a better understanding of what is going on in such systems. Our method can verify whether individual recommendations are reasonable regarding what the user has read in the past, and we can also use it to identify bad recommendations that do not come from any meaningful correspondence to previously read news.
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This page is a summary of: Explaining Neural News Recommendation with Attributions onto Reading Histories, ACM Transactions on Intelligent Systems and Technology, June 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3673233.
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