Improving Sequential Recommender Systems with Online and In-store User Behavior

TL;DR

This paper introduces a hybrid data pipeline and an encoder module to enhance sequential recommender systems by integrating online and in-store user behaviors, addressing the challenges of modeling hybrid user interactions.

Contribution

It proposes a novel omnichannel data pipeline and a model-agnostic encoder to effectively incorporate in-store behavior into online recommender systems.

Findings

Improved prediction accuracy for hybrid user behaviors.

Enhanced model capacity with the encoder module.

Seamless integration of online and in-store data sources.

Abstract

Online e-commerce platforms have been extending in-store shopping, which allows users to keep the canonical online browsing and checkout experience while exploring in-store shopping. However, the growing transition between online and in-store becomes a challenge to sequential recommender systems for future online interaction prediction due to the lack of holistic modeling of hybrid user behaviors (online and in-store). The challenges are twofold. First, combining online and in-store user behavior data into a single data schema and supporting multiple stages in the model life cycle (pre-training, training, inference, etc.) organically needs a new data pipeline design. Second, online recommender systems, which solely rely on online user behavior sequences, must be redesigned to support online and in-store user data as input under the sequential modeling setting. To overcome the first challenge, we propose a hybrid, omnichannel data pipeline to compile online and in-store user behavior data by caching information from diverse data sources. Later, we introduce a model-agnostic encoder module to the sequential recommender system to interpret the user in-store transaction and augment the modeling capacity for better online interaction prediction given the hybrid user behavior.