Model-Based Approaches for Independence-Enhanced Recommendation
Model-Based Approaches for Independence-Enhanced Recommendation
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DOI：
10.1109/icdmw.2016.0127
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发表时间：
2016-12
期刊：
影响因子：
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通讯作者：
Toshihiro Kamishima;S. Akaho;H. Asoh;Issei Sato
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作者：
Toshihiro Kamishima;S. Akaho;H. Asoh;Issei Sato
本文研究了一种提高推荐独立性的新方法。这些方法在确保遵守法律法规、公平对待内容提供者和排除不需要的信息方面非常有用。例如，从社会公平的角度来看，匹配雇主和求职者的推荐不应该基于性别或种族等社会敏感信息。在这种情况下，能够排除此类敏感信息影响的算法将是有用的。我们之前给出了推荐独立性的正式定义，并提出了一种采用正则化器施加这种独立性约束的方法。由于除了这种正则化方法之外没有其他选择，我们在这里提出了一种新的基于模型的方法，该方法基于满足推荐独立性约束的生成模型。我们将此方法应用于潜在类模型，并通过经验证明基于模型的方法可以增强推荐独立性。基于生成模型（如主题模型）的推荐算法很重要，因为它们具有灵活的功能，使它们能够包含各种信息类型。我们新的基于模型的方法将通过集成生成模型的功能来扩展独立性增强推荐的应用。
This paper studies a new approach to enhance recommendation independence. Such approaches are useful in ensuring adherence to laws and regulations, fair treatment of content providers, and exclusion of unwanted information. For example, recommendations that match an employer with a job applicant should not be based on socially sensitive information, such as gender or race, from the perspective of social fairness. An algorithm that could exclude the influence of such sensitive information would be useful in this case. We previously gave a formal definition of recommendation independence and proposed a method adopting a regularizer that imposes such an independence constraint. As no other options than this regularization approach have been put forward, we here propose a new model-based approach, which is based on a generative model that satisfies the constraint of recommendation independence. We apply this approach to a latent class model and empirically show that the model-based approach can enhance recommendation independence. Recommendation algorithms based on generative models, such as topic models, are important, because they have a flexible functionality that enables them to incorporate a wide variety of information types. Our new model-based approach will broaden the applications of independence-enhanced recommendation by integrating the functionality of generative models.