Modeling Multi-aspect Preferences and Intents for Multi-behavioral Sequential Recommendation

TL;DR

This paper introduces MAINT, an attentive recurrent model that captures multi-aspect user preferences and intents in multi-behavioral sequential recommendation, effectively handling noise and providing fine-grained insights.

Contribution

The paper proposes a novel multi-aspect projection and refinement attention mechanism to model detailed preferences and intents, addressing limitations of existing methods.

Findings

MAINT outperforms baseline models on real-world datasets.

The multi-aspect mechanisms effectively filter noise.

The model provides fine-grained, multi-dimensional user insights.

Abstract

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.