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arXiv2026

Multi-Task Deep Recommender Systems: A Survey

Multi-task learning (MTL) aims at learning related tasks in a unified model to achieve mutual improvement among tasks considering their shared knowledge. It is an important topic in recommendation due to the demand for multi-task prediction considering performance and efficiency. Although MTL has been well studied and developed, there is still a lack of systematic review in the recommendation community. To fill the gap, we provide a comprehensive review of existing multi-task deep recommender systems (MTDRS) in this survey. To be specific, the problem definition of MTDRS is first given, and it is compared with other related areas. Next, the development of MTDRS is depicted and the taxonomy is introduced from the task relation and methodology aspects. Specifically, the task relation is categorized into parallel, cascaded, and auxiliary with main, while the methodology is grouped into parameter sharing, optimization, and training mechanism. The survey concludes by summarizing the application and public datasets of MTDRS and highlighting the challenges and future directions of the field.

cs.IR↗

arXiv2026

Research on Intra-Chip Fusion Deployment and Optimization of Embodied Intelligence Business Operator NPU

Embodied intelligent computing integrates perception, computation and control. Traditional separate deployment of the three tasks leads to frequent data transmission, high latency and low hardware efficiency, failing to satisfy millisecond-level real-time requirements in dynamic scenarios. Besides, most operator optimization methods rely on foreign GPU platforms, while full-process collaborative optimization for domestic Phytium-Cambricon heterogeneous architectures is insufficient. This paper builds a domestic heterogeneous computing platform with Phytium FT-2000/4 processor and Cambricon MLU370 acceleration card, and proposes an NPU on-chip fusion deployment and full-process operator collaborative optimization strategy for perception, computation and control pipelines. Targeting embodied robot applications, modular optimization is conducted, including MLU hardware adaptation of motion blur correction operators for high-speed imaging, lightweight inference optimization of ViT models, and customized operator development for multi-DOF inverse kinematics solution. An on-chip data closed-loop and pipeline collaboration-based single-card solution is proposed to implement integrated execution of all perception-computation-control tasks on MLU370. Experimental results show that the proposed method achieves a full-process single-frame latency of 18.7 ms and a speedup of 2.89 compared with NVIDIA Jetson AGX Xavier, with 82.6% MLU utilization and comparable accuracy to mainstream platforms. This work offers a practical reference for domestic engineering applications of integrated embodied intelligent computing services.

cs.DC↗