许多推荐算法如基于矩阵分解因无法充分挖掘用户对项目的偏好信息而无法取得令人满意的推荐效果。为了解决上述问题,该文设计了两个模块,首先,利用多层感知机技术学习输入的信息以获得较好的特征表示,在原始输入时通过点积操作得到关系信息,并将其命名为深度矩阵分解(DeepMF);其次,在多层感知机中加入多层注意力网络,这样能够得到用户对项目的偏好信息。此外,点积操作应用于输出前是为了获得特征表达的关系信息,这一模块名为深度注意力矩阵分解(DeepAMF)。通过结合两个模块的优势得到联合多层注意力网络矩阵分解算法(MAMF),在四个公开数据集上的实验证明了MAMF算法的有效性。
Abstract
To further improve current recommendation algorithms, such as Matrix Factorization, a method of Deep Attention Matrix Factorization (DeepAMF) are introduced in this paper. First, the multi-layer perceptron technology is applied to obtain a better feature representation and got the relational information through the dot product operation during the original input, which are named as Deep Matrix Factorization (DeepMF). Then multi-layer attention network is exploited to to obtain the user's preference for the item. Besides, the dot product operation is applied before the output to obtain the related information of the feature expression. And the module was called. Experiments on four public data sets prove the effectiveness of the MAMF algorithm.
关键词
矩阵分解 /
多层注意力 /
联合模型
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Key words
matrix factorization /
multi-layer attention /
joint model
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脚注
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基金
安徽理工大学校级重点项目(xjzd2020-15);国家自然科学基金(61702003);安徽省自然科学基金(1808085MF175);安徽省协同创新项目(GXXT-2019-018);安徽省教育厅高校自然科学研究项目(KJ2020A0289);人工智能与机器人省级实验实训中心项目(2020sxzx08)
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