Recently, Multi-Scenario Learning (MSL) is widely used in recommendation and retrieval systems in the industry because it facilitates transfer learning from different scenarios, mitigating data sparsity and reducing maintenance cost. These efforts produce different MSL paradigms by searching more optimal network structure, such as Auxiliary Network, Expert Network, and Multi-Tower Network. It is intuitive that different scenarios could hold their specific characteristics, activating the user's intents quite differently. In other words, different kinds of auxiliary features would bear varying importance under different scenarios. With more discriminative feature representations refined in a scenario-aware manner, better ranking performance could be easily obtained without expensive search for the optimal network structure. Unfortunately, this simple idea is mainly overlooked but much desired in real-world systems.Further analysis also validates the rationality of adaptive feature learning under a multi-scenario scheme. Moreover, our A/B test results on the Alibaba search advertising platform also demonstrate that Maria is superior in production environments.

翻译：近年来，多场景学习（MSL）因能促进不同场景间的迁移学习、缓解数据稀疏性并降低维护成本，在工业界的推荐与检索系统中得到广泛应用。现有研究通过探索更优网络结构（如辅助网络、专家网络、多塔网络）形成了不同的MSL范式。直觉表明，不同场景可能具有特定特征，以截然不同的方式激活用户意图。换言之，各类辅助特征在不同场景下的重要性存在差异。若以场景感知方式获得更具判别性的特征表示，无需昂贵地搜索最优网络结构即可轻松提升排序性能。遗憾的是，这一简洁思路在实际系统中虽备受期待却常被忽视。进一步分析验证了多场景框架下自适应特征学习的合理性。此外，我们在阿里巴巴搜索广告平台上的A/B测试结果证明，Maria在生产环境中具有显著优势。