Year: 2024
Type: conference-paper
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 online sequential recommender systems for future online interaction prediction due to the lack of holistic modeling of hybrid user behaviors (online & in-store). The challenges are two-fold. First, combining online & 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 relies 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 & 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.
Authors: Luyi Ma, Aashika Padmanabhan, Anjana Ganesh, Shengwei Tang, Jiao Chen, Xiaohan Li, Lalitesh Morishetti, Kaushiki Nag, Malay Patel, Jason Cho, Sushant Kumar, Kannan Achan
Institution: Walmart (United States)
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4406458145