Robust Recommender Systems with Rating Flip Noise

IF 7.2 4区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Shanshan Ye, Jie Lu
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引用次数: 0

Abstract

Recommender systems have become important tools in the daily life of human beings since they are powerful to address information overload, and discover relevant and useful items for users. The success of recommender systems largely relies on the interaction history between users and items, which is expected to accurately reflect the preferences of users on items. However, the expectation is easily broken in practice, due to the corruptions made in the interaction history, resulting in unreliable and untrusted recommender systems. Previous works either ignore this issue (assume that the interaction history is precise) or are limited to handling additive noise. Motivated by this, in this paper, we study rating flip noise which is widely existed in the interaction history of recommender systems and combat it by modelling the noise generation process. Specifically, the rating flip noise allows a rating to be flipped to any other ratings within the given rating set, which reflects various real-world situations of rating corruption, e.g., a user may randomly click a rating from the rating set and then submit it. The noise generation process is modelled by the noise transition matrix that denotes the probabilities of a clean rating flip into a noisy rating. A statistically consistent algorithm is afterwards applied with the estimated transition matrix to learn a robust recommender system against rating flip noise. Comprehensive experiments on multiple benchmarks confirm the superiority of our method.

具有评级翻转噪声的鲁棒推荐系统
推荐系统能够解决信息过载问题,为用户发现相关的有用信息,因此已成为人类日常生活中的重要工具。推荐系统的成功在很大程度上依赖于用户与物品之间的交互历史,而用户与物品之间的交互历史有望准确反映用户对物品的偏好。然而,在实践中,由于交互历史的破坏,这种期望很容易被打破,从而导致推荐系统不可靠、不可信。以往的研究要么忽略了这个问题(假设交互历史是精确的),要么仅限于处理加性噪声。受此启发,我们在本文中研究了广泛存在于推荐系统交互历史中的评分翻转噪声,并通过模拟噪声产生过程来解决这一问题。具体来说,评分翻转噪声允许一个评分翻转到给定评分集内的任何其他评分,这反映了现实世界中各种评分损坏的情况,例如,用户可能会随机点击评分集中的一个评分,然后提交它。噪声生成过程由噪声转换矩阵模拟,该矩阵表示干净评分翻转为噪声评分的概率。然后,利用估算出的过渡矩阵应用统计学上一致的算法来学习鲁棒的推荐系统,以抵御评分翻转噪声。多个基准的综合实验证实了我们方法的优越性。
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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