Multi-behavioral sequential recommendation has recently attracted increasing attention. However, existing methods suffer from two major limitations. Firstly, user preferences and intents can be described in fine-grained detail from multiple perspectives; yet, these methods fail to capture their multi-aspect nature. Secondly, user behaviors may contain noises, and most existing methods could not effectively deal with noises. In this paper, we present an attentive recurrent model with multiple projections to capture Multi-Aspect preferences and INTents (MAINT in short). To extract multi-aspect preferences from target behaviors, we propose a multi-aspect projection mechanism for generating multiple preference representations from multiple aspects. To extract multi-aspect intents from multi-typed behaviors, we propose a behavior-enhanced LSTM and a multi-aspect refinement attention mechanism. The attention mechanism can filter out noises and generate multiple intent representations from different aspects. To adaptively fuse user preferences and intents, we propose a multi-aspect gated fusion mechanism. Extensive experiments conducted on real-world datasets have demonstrated the effectiveness of our model.

翻译：多行为序列推荐最近引起了越来越多的关注。然而，现有方法存在两个主要局限性。首先，用户偏好和意图可以从多个角度进行细粒度描述；但这些方法未能捕捉其多方面的特性。其次，用户行为可能包含噪声，而大多数现有方法无法有效处理这些噪声。在本文中，我们提出了一种具有多重投影的注意力循环模型，以捕捉多方面偏好与意图（简称MAINT）。为了从目标行为中提取多方面偏好，我们提出了一种多方面投影机制，用于从多个方面生成多个偏好表示。为了从多种类型的行为中提取多方面意图，我们提出了一种行为增强的LSTM和一种多方面细化注意力机制。该注意力机制能够过滤噪声，并从不同方面生成多个意图表示。为了自适应地融合用户偏好和意图，我们提出了一种多方面门控融合机制。在真实世界数据集上进行的大量实验证明了我们模型的有效性。