用阅读历史归因解释神经新闻推荐

IF 7.2 4区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE ACM Transactions on Intelligent Systems and Technology Pub Date : 2024-06-18 DOI:10.1145/3673233
Lucas Möller, Sebastian Padó
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引用次数: 0

摘要

负责任的推荐系统的一个重要方面是预测机制的透明度。这是基于深度学习的系统面临的普遍挑战,例如目前占主导地位的神经新闻推荐架构,通过将候选新闻条目与用户的阅读历史相匹配来优化点击预测。这类系统实现了最先进的点击预测性能,但其决策的合理性却难以评估。在本文中,我们提出了这样一个问题:当前新闻推荐系统的推荐在多大程度上是基于阅读历史中与内容相关的证据。我们从可解释性的角度来探讨这个问题。在综合梯度概念的基础上,我们提出了一种神经新闻推荐器,它可以准确地将单个推荐归因于输入阅读历史中的新闻条目和单词,同时保持最高得分的点击预测性能:利用我们的方法作为诊断工具,我们发现:(a) 大量用户对新闻的点击无法从阅读历史中得到解释,许多可从历史中得到解释的项目实际上被跳过;(b) 尽管许多推荐基于历史中与内容相关的证据,但对于其他内容,模型并未关注合理的证据,推荐源于用户表征中的虚假偏差。我们的代码已公开发布1。
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Explaining Neural News Recommendation with Attributions onto Reading Histories

An important aspect of responsible recommendation systems is the transparency of the prediction mechanisms. This is a general challenge for deep-learning-based systems such as the currently predominant neural news recommender architectures which are optimized to predict clicks by matching candidate news items against users’ reading histories. Such systems achieve state-of-the-art click-prediction performance, but the rationale for their decisions is difficult to assess. At the same time, the economic and societal impact of these systems makes such insights very much desirable.

In this paper, we ask the question to what extent the recommendations of current news recommender systems are actually based on content-related evidence from reading histories. We approach this question from an explainability perspective. Building on the concept of integrated gradients, we present a neural news recommender that can accurately attribute individual recommendations to news items and words in input reading histories while maintaining a top scoring click-prediction performance.

Using our method as a diagnostic tool, we find that: (a), a substantial number of users’ clicks on news are not explainable from reading histories, and many history-explainable items are actually skipped; (b), while many recommendations are based on content-related evidence in histories, for others the model does not attend to reasonable evidence, and recommendations stem from a spurious bias in user representations. Our code is publicly available1.

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来源期刊
ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.30
自引率
2.00%
发文量
131
期刊介绍: ACM Transactions on Intelligent Systems and Technology is a scholarly journal that publishes the highest quality papers on intelligent systems, applicable algorithms and technology with a multi-disciplinary perspective. An intelligent system is one that uses artificial intelligence (AI) techniques to offer important services (e.g., as a component of a larger system) to allow integrated systems to perceive, reason, learn, and act intelligently in the real world. ACM TIST is published quarterly (six issues a year). Each issue has 8-11 regular papers, with around 20 published journal pages or 10,000 words per paper. Additional references, proofs, graphs or detailed experiment results can be submitted as a separate appendix, while excessively lengthy papers will be rejected automatically. Authors can include online-only appendices for additional content of their published papers and are encouraged to share their code and/or data with other readers.
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