A Unified Search and Recommendation Framework Based on Multi-Scenario Learning for Ranking in E-commerce
<|reference_start|>A Unified Search and Recommendation Framework Based on Multi-Scenario Learning for Ranking in E-commerce: Search and recommendation (S&R) are the two most important scenarios in e-commerce. The majority of users typically interact with products in S&R scenarios, indicating the need and potential for joint modeling. Traditional multi-scenario models use shared parameters to learn the similarity of multiple tasks, and task-specific parameters to learn the divergence of individual tasks. This coarse-grained modeling approach does not effectively capture the differences between S&R scenarios. Furthermore, this approach does not sufficiently exploit the information across the global label space. These issues can result in the suboptimal performance of multi-scenario models in handling both S&R scenarios. To address these issues, we propose an effective and universal framework for Unified Search and Recommendation (USR), designed with S&R Views User Interest Extractor Layer (IE) and S&R Views Feature Generator Layer (FG) to separately generate user interests and scenario-agnostic feature representations for S&R. Next, we introduce a Global Label Space Multi-Task Layer (GLMT) that uses global labels as supervised signals of auxiliary tasks and jointly models the main task and auxiliary tasks using conditional probability. Extensive experimental evaluations on real-world industrial datasets show that USR can be applied to various multi-scenario models and significantly improve their performance. Online A/B testing also indicates substantial performance gains across multiple metrics. Currently, USR has been successfully deployed in the 7Fresh App.<|reference_end|>
@article{liu2024a,
    title={A Unified Search and Recommendation Framework Based on Multi-Scenario
  Learning for Ranking in E-commerce},
    author={Jinhan Liu, Qiyu Chen, Junjie Xu, Junjie Li, Baoli Li, Sulong Xu},
    journal={arXiv preprint arXiv:2405.10835},
    year={2024},
    doi={10.1145/3626772.3661356},
    archivePrefix={arXiv},
    eprint={2405.10835},
    primaryClass={cs.IR}
}