平衡质量得分(BQS):衡量推荐中的人气衰减

IF 7.2 4区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Erica Coppolillo, Marco Minici, Ettore Ritacco, Luciano Caroprese, Francesco Sergio Pisani, Giuseppe Manco
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引用次数: 0

摘要

受欢迎程度偏差是指推荐系统倾向于进一步推荐受欢迎的项目,而忽略小众项目,从而使受欢迎程度低的项目没有机会出现。尽管文献中包含了丰富的去偏差技术,但仍然缺乏有效的质量衡量标准来对其进行分析和比较。在本文中,我们首先介绍了一种正式的、数据驱动的、无参数策略,用于将项目分为低、中、高人气类别。然后,我们介绍了 BQS,这是一种质量度量方法,用于奖励成功推动推荐系统推荐小众项目的去弱化技术,同时又不降低其在全局准确性方面的预测能力。我们在三个不同的基线协同过滤(CF)框架上对 BQS 进行了测试:一个基于历史嵌入,两个基于用户/项目嵌入建模。这些评估是在多个基准数据集上进行的,并与各种最先进的竞争对手进行了比较,从而证明了 BQS 的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Balanced Quality Score (BQS): Measuring Popularity Debiasing in Recommendation

Popularity bias is the tendency of recommender systems to further suggest popular items while disregarding niche ones, hence giving no chance for items with low popularity to emerge. Although the literature is rich in debiasing techniques, it still lacks quality measures that effectively enable their analyses and comparisons.

In this paper, we first introduce a formal, data-driven, and parameter-free strategy for classifying items into low, medium, and high popularity categories. Then we introduce BQS, a quality measure that rewards the debiasing techniques that successfully push a recommender system to suggest niche items, without losing points in its predictive capability in terms of global accuracy.

We conduct tests of BQS on three distinct baseline collaborative filtering (CF) frameworks: one based on history-embedding and two on user/item-embedding modeling. These evaluations are performed on multiple benchmark datasets and against various state-of-the-art competitors, demonstrating the effectiveness of BQS.

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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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