On-device machine learning enables the lightweight deployment of recommendation models in local clients, which reduces the burden of the cloud-based recommenders and simultaneously incorporates more real-time user features. Nevertheless, the cloud-based recommendation in the industry is still very important considering its powerful model capacity and the efficient candidate generation from the billion-scale item pool. Previous attempts to integrate the merits of both paradigms mainly resort to a sequential mechanism, which builds the on-device recommender on top of the cloud-based recommendation. However, such a design is inflexible when user interests dramatically change:
the on-device model is stuck by the limited item cache while the cloud-based recommendation based on the large item pool do not respond without the new re-fresh feedback.
To overcome this issue, we propose a meta controller to dynamically manage the collaboration between the on-device recommender and the cloud-based recommender, and introduce a novel efficient sample construction from the causal perspective to solve the dataset absence issue of meta controller. On the basis of the counterfactual samples and the extended training, extensive experiments in the industrial recommendation scenarios show the promise of meta controller in the device-cloud collaboration.
MBP-KT: Learning Global Collaborative Information from Meta-Behavioral Pattern for Enhanced Knowledge Tracing
Faculty of Artificial Intelligence in Education, Central China Normal University（中央财经大学教育人工智能学院）；School of Information Management, Wuhan University（武汉大学信息管理学院）；School of Big Data and Artificial Intelligence, Chizhou University（池州学院大数据与人工智能学院）
MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework
DeepWisdom（深度智慧）；AI Initiative, King Abdullah University of Science and Technology（阿卜杜拉国王科技大学人工智能计划）；Xiamen University（厦门大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Nanjing University（南京大学）；University of Pennsylvania（宾夕法尼亚大学）；University of California, Berkeley（加州大学伯克利分校）；The Swiss AI Lab IDSIA/USI/SUPSI（瑞士IDSIA/USI/SUPSI人工智能实验室）
RRCM: Ranking-Driven Retrieval over Collaborative and Meta Memories for LLM Recommendation
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Illinois at Chicago（伊利诺伊大学香槟分校）；Capital One AI Foundations（Capital One AI基金会）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Collaborative Imputation of Urban Time Series through Cross-city Meta-learning
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University（香港理工大学土木与环境工程系）；Department of Traffic Engineering, Tongji University（同济大学交通工程系）；Centre for Learning, Teaching, and Technology, The Education University of Hong Kong（香港教育大学学习、教学与科技中心）；Research Institute for Artificial Intelligence of Things, Department of Computing, The Hong Kong Polytechnic University（香港理工大学人工智能物联网研究所）
