Correcting Popularity Bias by Enhancing Recommendation Neutrality
Correcting Popularity Bias by Enhancing Recommendation Neutrality
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发表时间：
2014
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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
在本文中，我们试图纠正流行的偏见，这是流行的项目被推荐更频繁的趋势，通过增强推荐中立性。推荐中立性是指在推荐的预测过程中排除特定的信息。这种中立性被形式化为推荐结果与指定信息之间的统计独立性，并且我们开发了满足这种独立性约束的推荐算法。我们通过增强关于候选项目是否受欢迎的信息的中立性来纠正受欢迎的偏见。我们的经验表明，在预测的偏好得分的流行偏见是可以纠正的。
In this paper, we attempt to correct a popularity bias, which is the tendency for popular items to be recommended more frequently, by enhancing recommendation neutrality. Recommendation neutrality involves excluding specified information from the prediction process of recommendation. This neutrality was formalized as the statistical independence between a recommendation result and the specified information, and we developed a recommendation algorithm that satisfies this independence constraint. We correct the popularity bias by enhancing neutrality with respect to information regarding whether candidate items are popular or not. We empirically show that a popularity bias in the predicted preference scores can be corrected.