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

• URL: http://arxiv.org/abs/2412.02122v1
• Date: Tue, 03 Dec 2024 03:20:40 GMT
• Title: Improving Sequential Recommender Systems with Online and In-store 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,
• Abstract summary: We propose a hybrid, omnichannel data pipeline to compile online and in-store user behavior data by caching information from diverse data sources.<n>We introduce a model-agnostic encoder module to the sequential recommender system to interpret the user in-store transaction.
• Score: 8.606708734834623
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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