Mixed similarity learning for recommendation with implicit feedback
Mixed similarity learning for recommendation with implicit feedback
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DOI：
10.1016/j.knosys.2016.12.010
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发表时间：
2017-03
期刊：
影响因子：
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通讯作者：
Mengsi Liu;Weike Pan;Miao Liu;Yaofeng Chen;Xiaogang Peng;Zhong Ming
中科院分区：
文献类型：
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作者：
Mengsi Liu;Weike Pan;Miao Liu;Yaofeng Chen;Xiaogang Peng;Zhong Ming
在大多数推荐场景中，用户的检查行为等隐式反馈被认为是非常重要的信息来源。对于具有隐式反馈的推荐，良好的相似性度量和适当的偏好假设对个性化服务的质量至关重要。到目前为止，最先进的推荐方法中使用的相似度包括预定义相似度和学习相似度；偏好假设包括著名的两两假设。本文通过一种新的混合相似度模型，利用预定义相似度和学习相似度的互补性。在此基础上，基于混合相似度和配对偏好假设，提出了一种新的推荐算法，即成对因子混合相似模型（pairwise factors mixed similarity model, P-FMSM）。我们的P-FMSM能够(i)通过对称的预定义相似度捕获用户-项目交互的局部性，（ii）通过非对称的学习相似度建模项目之间的全局相关性，以及（iii）通过配对偏好假设消化不确定的隐含反馈。对四个公共数据集的实证研究表明，我们的P-FMSM可以比几种最先进的方法更准确地推荐。
Implicit feedback such as users’ examination behaviors have been recognized as a very important source of information in most recommendation scenarios. For recommendation with implicit feedback, a good similarity measurement and a proper preference assumption are critical for the quality of personalization services. So far, the similarities used in the state-of-the-art recommendation methods include thepredefined similarityand thelearned similarity; and the preference assumptions include the well-knownpairwise assumption.In this paper, we exploit the complementarity of the predefined similarity and the learned similarity via a novel mixed similarity model. Furthermore, we develop a novel recommendation algorithm, i.e.,pairwise factored mixed similarity model (P-FMSM), based on the mixed similarity and pairwise preference assumption. Our P-FMSM is able to (i) capture the locality of the user-item interactions via the symmetric predefined similarity, (ii) model the global correlations among items via the asymmetric learned similarity, and (iii) digest the uncertain implicit feedback via the pairwise preference assumption. Empirical studies on four public datasets show that our P-FMSM can recommend significantly more accurate than several state-of-the-art methods.