Leveraging Decomposed Trust in Probabilistic Matrix Factorization for Effective Recommendation
Leveraging Decomposed Trust in Probabilistic Matrix Factorization for Effective Recommendation
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DOI：
10.1609/aaai.v28i1.8714
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发表时间：
2014-06
期刊：
影响因子：
--
通讯作者：
Hui Fang;Yang Bao;Jie Zhang
中科院分区：
文献类型：
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作者：
Hui Fang;Yang Bao;Jie Zhang
在推荐系统中，信任被用来替代或补充基于评级的相似度，以提高评级预测的准确性。然而，相互信任的人可能并不总是有相似的偏好。在本文中，我们试图填补这一空白，将原始的单一方面的信任信息分解为四个一般信任方面，即仁爱、诚信、能力和可预测性，并进一步利用支持向量回归技术将其纳入到概率矩阵分解模型中，用于推荐系统的评级预测。在四个数据集上的实验结果表明，我们的方法优于最先进的方法。
Trust has been used to replace or complement rating-based similarity in recommender systems, to improve the accuracy of rating prediction. However, people trusting each other may not always share similar preferences. In this paper, we try to fill in this gap by decomposing the original single-aspect trust information into four general trust aspects, i.e. benevolence, integrity, competence, and predictability, and further employing the support vector regression technique to incorporate them into the probabilistic matrix factorization model for rating prediction in recommender systems. Experimental results on four datasets demonstrate the superiority of our method over the state-of-the-art approaches.