User behavior understanding in real world settings

TL;DR

This paper introduces AutoRep, a novel model that adaptively generates dynamic user behavior representations, improving recommendation accuracy by handling diverse and changing item groups in user histories.

Contribution

AutoRep's innovative design automatically and adaptively constructs dynamic user behavior representations, surpassing fixed methods and enhancing recommendation performance.

Findings

AutoRep outperforms baseline models on five benchmark datasets.

The IRC module captures overall sequential user behavior characteristics.

The DRC module effectively allocates items into dynamic groups.

Abstract

How to extract meaningful information in user historical behavior plays a crucial role in recommendation. User behavior sequence often contains multiple conceptually distinct items that belong to different item groups and the number of the item groups is changing over time. It is necessary to learn a dynamic group of representations according the item groups in a user historical behavior. However, current works only learns a predefined and fixed number representations which includes single representation methods and multi representations methods from the user context that could lead to suboptimal recommendation quality. In this paper we propose a model that can automatically and adaptively generates a dynamic group of representations from the user behavior accordingly. To be specific, AutoRep is composed of an informative representation construct (IRC) module and a dynamic representations construct (DRC) module. The IRC module learns the overall sequential characteristics of user behavior with a bi-directional architecture transformer. The DRC module dynamically allocate the item in the user behavior into different item groups and form a dynamic group of representations in a differentiable method. Such design improves the model recommendation performance. We evaluate the proposed model on five benchmark datasets. The results show that AutoRep outperforms representative baselines. Further ablation study has been conducted to deepen our understandings of AutoRep, including the proposed module IRC and DRC.