  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.
