计算机科学 ›› 2022, Vol. 49 ›› Issue (12): 163-169.doi: 10.11896/jsjkx.211200080
伍美霖1, 黄佳进2, 秦进1
WU Mei-lin1, HUANG Jia-jin2, QIN Jin1
摘要: 推荐模型通常使用用户的历史行为来获得用户偏好表示,以产生推荐。大多数方法学习到的用户表示会把不同的偏好因素纠缠在一起,而解耦学习的方法可以用于分解用户的行为特征。为此,文中提出了一个基于变分自编码器的框架DSVAECF,用于从用户历史行为中分解静态和动态偏好因素。首先,DSVAECF模型的两个编码器分别使用多层感知机和循环神经网络对用户行为进行历史行为建模,以此得到用户的静态和动态偏好表示;然后,将拼接的静态和动态偏好表示视为用户偏好的解耦表示,并将其输入解码器来捕获用户的决策,并重构出用户行为。在模型训练阶段,一方面最大化重构的用户行为与真实用户行为之间的互信息来学习模型参数;另一方面通过最小化解耦表示与其先验分布间的差异来保留模型的生成能力。在Amazon和MovieLens两个数据集上的实验结果表明,与基准方法相比,DSVAECF在归一化折损累计增益、精确率和召回率上都有显著的提升,拥有更好的推荐性能。
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