论文标题
一流协作过滤中的多方面偏好学习的专注自动编码器
Attentive Autoencoders for Multifaceted Preference Learning in One-class Collaborative Filtering
论文作者
论文摘要
大多数现有的单级协作过滤（OC-CF）算法通过编码其历史交互来估计用户作为潜在向量的偏好。但是，用户经常表现出多种兴趣，这大大增加了学习难度。为了捕获多方面的用户偏好，现有的推荐系统要么增加编码复杂性或扩展潜在表示维度。不幸的是，这些变化不可避免地会导致训练难度增加，并加剧了可伸缩性问题。在本文中，我们提出了一个新颖有效的CF框架，称为Activentive Multi-Modal Autorec（AMA），该框架明确跟踪用户偏好的多个方面。具体而言，我们扩展了基于自动编码的推荐人AUTOREC，以学习具有多模式潜在表示的用户首选项，其中每种模式捕获了用户偏好的一个方面。通过利用注意机制，每个观察到的相互作用可以对偏好方面有不同的贡献。通过在三个现实世界数据集上进行的大量实验，我们表明AMA在OC-CF设置下与最新模型具有竞争力。另外，我们证明了提出的模型如何通过使用注意机制提供解释来提高可解释性。
Most existing One-Class Collaborative Filtering (OC-CF) algorithms estimate a user's preference as a latent vector by encoding their historical interactions. However, users often show diverse interests, which significantly increases the learning difficulty. In order to capture multifaceted user preferences, existing recommender systems either increase the encoding complexity or extend the latent representation dimension. Unfortunately, these changes inevitably lead to increased training difficulty and exacerbate scalability issues. In this paper, we propose a novel and efficient CF framework called Attentive Multi-modal AutoRec (AMA) that explicitly tracks multiple facets of user preferences. Specifically, we extend the Autoencoding-based recommender AutoRec to learn user preferences with multi-modal latent representations, where each mode captures one facet of a user's preferences. By leveraging the attention mechanism, each observed interaction can have different contributions to the preference facets. Through extensive experiments on three real-world datasets, we show that AMA is competitive with state-of-the-art models under the OC-CF setting. Also, we demonstrate how the proposed model improves interpretability by providing explanations using the attention mechanism.
