提出高效可扩展的通用推荐模型，解决多任务学习性能下降问题。
Efficient Multi-Task Learning via Generalist Recommender
- 用NLP头与并行Transformer处理多模态输入，构建统一架构。
- 引入任务-句子级路由机制，在多任务下保持性能不降。
- 已在大型电信平台落地，支持高并发在线流量。
多任务学习（MTL）是一种常见机器学习技术，可通过跨任务共享信息提升所有任务的推荐精度。然而，现有大多数MTL实现随着任务数量增加会出现训练与推理性能下降，限制了其在生产环境的应用。受大语言模型进展启发，我们提出一种端到端高效且可扩展的通用推荐模型（GRec）。GRec通过NLP头部、并行Transformer以及宽深结构综合处理多模态输入，并引入新型任务-句子级路由机制，使模型在多任务场景下具备可扩展能力而无需牺牲性能。离线评估与在线实验均表明，GRec显著优于此前推荐系统方案。该模型已成功部署于全球最大电信网站及应用中，每日有效处理海量在线流量。
原文摘要 · Abstract (English)
Multi-task learning (MTL) is a common machine learning technique that allows the model to share information across different tasks and improve the accuracy of recommendations for all of them. Many existing MTL implementations suffer from scalability issues as the training and inference performance can degrade with the increasing number of tasks, which can limit production use case scenarios for MTL-based recommender systems. Inspired by the recent advances of large language models, we developed an end-to-end efficient and scalable Generalist Recommender (GRec). GRec takes comprehensive data signals by utilizing NLP heads, parallel Transformers, as well as a wide and deep structure to process multi-modal inputs. These inputs are then combined and fed through a newly proposed task-sentence level routing mechanism to scale the model capabilities on multiple tasks without compromising performance. Offline evaluations and online experiments show that GRec significantly outperforms our previous recommender solutions. GRec has been successfully deployed on one of the largest telecom websites and apps, effectively managing high volumes of online traffic every day.
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