Improving Sequential Recommender Systems with Online and In-store User Behavior
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866929611692048384 |
|---|---|
| author | Ma, Luyi Padmanabhan, Aashika Ganesh, Anjana Tang, Shengwei Chen, Jiao Li, Xiaohan Morishetti, Lalitesh Nag, Kaushiki Patel, Malay Cho, Jason Kumar, Sushant Achan, Kannan |
| author_facet | Ma, Luyi Padmanabhan, Aashika Ganesh, Anjana Tang, Shengwei Chen, Jiao Li, Xiaohan Morishetti, Lalitesh Nag, Kaushiki Patel, Malay Cho, Jason Kumar, Sushant Achan, Kannan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02122 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving Sequential Recommender Systems with Online and In-store User Behavior Ma, Luyi Padmanabhan, Aashika Ganesh, Anjana Tang, Shengwei Chen, Jiao Li, Xiaohan Morishetti, Lalitesh Nag, Kaushiki Patel, Malay Cho, Jason Kumar, Sushant Achan, Kannan Information Retrieval 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. |
| title | Improving Sequential Recommender Systems with Online and In-store User Behavior |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2412.02122 |