Efficiency Improvement of Neutrality-Enhanced Recommendation
Efficiency Improvement of Neutrality-Enhanced Recommendation
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发表时间：
2013
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通讯作者：
Toshihiro Kamishima;S. Akaho;H. Asoh;Jun Sakuma
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作者：
Toshihiro Kamishima;S. Akaho;H. Asoh;Jun Sakuma
本文提出了一种推荐算法，以增强用户指定观点的中立性。该算法有助于避免基于有偏见信息的决策。这种问题被称为过滤气泡，即个性化技术对社会决策的影响。为了提供中立性增强的推荐，我们必须首先假设用户可以指定可以应用中立性的特定观点，因为从所有观点来看都是中立的推荐不再是推荐。给定这样的目标视点，我们通过引入惩罚项来强制目标视点和评级之间的统计独立性来实现信息中立的推荐算法。经验表明，该算法增强了给定视点的独立性。
This paper proposes an algorithm for making recommendations so that neutrality from a viewpoint specified by the user is enhanced. This algorithm is useful for avoiding decisions based on biased information. Such a problem is pointed out as the filter bubble, which is the influence in social decisions biased by personalization technologies. To provide a neutrality-enhanced recommendation, we must first assume that a user can specify a particular viewpoint from which the neutrality can be applied, because a recommendation that is neutral from all viewpoints is no longer a recommendation. Given such a target viewpoint, we implement an information-neutral recommendation algorithm by introducing a penalty term to enforce statistical independence between the target viewpoint and a rating. We empirically show that our algorithm enhances the independence from the specified viewpoint.