  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.
