Mixture-preference bayesian matrix factorization for implicit feedback datasets
Mixture-preference bayesian matrix factorization for implicit feedback datasets
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DOI：
10.1145/3341105.3375755
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发表时间：
2020-03
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通讯作者：
Shaowen Peng;Tsunenori Mine
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文献类型：
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作者：
Shaowen Peng;Tsunenori Mine
近年来，隐反馈推荐技术得到了广泛的研究。与显式反馈的推荐相比，向用户提供稳定和准确的推荐更加困难，这是由于来自隐式反馈数据集的交互不能清楚地指示用户偏好的水平。大多数现有的方法处理隐式反馈取得了优异的性能，专注于其他方面，而不是直接推断用户的喜好。在本文中，我们提供准确的推荐给用户解决的问题，直接推断用户的偏好，从隐式反馈的信息少，巨大的不确定性。本文提出了一种新的混合偏好模型（MPBMF），该模型引入了一组伪偏好值来模拟用户的真实偏好。更具体地说，我们提出的模型可以被描述为高斯混合模型，其中每个模型都是用伪偏好训练的，这些伪偏好显示了用户对项目的不同看法。然后在不同贡献度的不同伪偏好下，利用模型估计预测的用户偏好。我们在三个真实数据集上进行了大量的实验，其上级性能证明了我们模型的有效性。
Recommendation with implicit feedback has been extensively studied in recent years. It is more difficult to provide users with stable and accurate recommendation compared to recommendation with explicit feedback, due to the reason that interactions from implicit feedback datasets do not clearly indicate the level of user preference. Most existing methods dealing with implicit feedback have achieved excellent performance by focusing on other aspects rather than directly inferring user preference. In this paper, we offer accurate recommendation to users by addressing the problem of directly inferring user preference from implicit feedback with such less information and huge uncertainty. We propose a novel mixture-preference model (MPBMF), which introduces a set of pseudo-preference values to surmise the true user preference. More specifically our proposed model can be described as a Gaussian mixture model in which each single model is trained with pseudo-preferences which show the user's different views for items. Then the predicted user preference is estimated by the models under different pseudo-preferences with different contributions. We conduct extensive experiments on three real-world datasets, and the superior performance demonstrates the effectiveness of our model.