Improving Faithfulness and Factuality with Contrastive Learning in Explainable Recommendation

IF 7.2 4区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Haojie Zhuang, Wei Zhang, Weitong Chen, Jian Yang, Quan Z. Sheng
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引用次数: 0

Abstract

Recommender systems have become increasingly important in navigating the vast amount of information and options available in various domains. By tailoring and personalizing recommendations to user preferences and interests, these systems improve the user experience, efficiency and satisfaction. With a growing demand for transparency and understanding of recommendation outputs, explainable recommender systems have gained growing attention in recent years. Additionally, as user reviews could be considered the rationales behind why the user likes (or dislikes) the products, generating informative and reliable reviews alongside recommendations has thus emerged as a research focus in explainable recommendation. However, the model-generated reviews might contain factual inconsistent contents (i.e., the hallucination issue), which would thus compromise the recommendation rationales. To address this issue, we propose a contrastive learning framework to improve the faithfulness and factuality in explainable recommendation in this paper. We further develop different strategies of generating positive and negative examples for contrastive learning, such as back-translation or synonym substitution for positive examples, and editing positive examples or utilizing model-generated texts for negative examples. Our proposed method optimizes the model to distinguish faithful explanations (i.e., positive examples) and unfaithful ones with factual errors (i.e., negative examples), which thus drives the model to generate faithful reviews as explanations while avoiding inconsistent contents. Extensive experiments and analysis on three benchmark datasets show that our proposed model outperforms other review generation baselines in faithfulness and factuality. In addition, the proposed contrastive learning component could be easily incorporated into other explainable recommender systems in a plug-and-play manner.

在可解释的推荐中通过对比学习提高忠实性和事实性
在浏览各领域的大量信息和选项时,推荐系统变得越来越重要。通过根据用户偏好和兴趣定制个性化推荐,这些系统可以改善用户体验,提高效率和满意度。随着人们对推荐结果的透明度和理解力的要求越来越高,可解释的推荐系统近年来受到越来越多的关注。此外,由于用户评论可被视为用户喜欢(或不喜欢)产品背后的理由,因此在推荐的同时生成信息丰富且可靠的评论已成为可解释推荐的研究重点。然而,模型生成的评论可能会包含与事实不符的内容(即幻觉问题),从而影响推荐的合理性。为了解决这个问题,我们在本文中提出了一个对比学习框架,以提高可解释推荐的忠实性和事实性。我们进一步开发了用于对比学习的生成正面和负面示例的不同策略,例如正面示例的回译或同义词替换,负面示例的编辑正面示例或利用模型生成的文本。我们提出的方法优化了模型,以区分忠实的解释(即正面例子)和有事实错误的不忠实解释(即负面例子),从而促使模型生成忠实的评论作为解释,同时避免不一致的内容。在三个基准数据集上进行的大量实验和分析表明,我们提出的模型在忠实性和事实性方面优于其他评论生成基线。此外,我们提出的对比学习组件可以即插即用的方式轻松地集成到其他可解释的推荐系统中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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