Results 271 to 280 of about 3,999,655 (320)

LCS : Alleviating Total Cold Start Latency in Serverless Applications with LRU Warm Container Approach

International Conference of Distributed Computing and Networking, 2023
Serverless computing offers "Function-as-a-Service"(FaaS), which promotes an application in the form of independent granular components called functions.
Biswajeet Sethi, S. K. Addya, S. Ghosh
semanticscholar   +1 more source

Advancing Multimodal Reasoning via Reinforcement Learning with Cold Start

arXiv.org
Recent advancements in large language models (LLMs) have demonstrated impressive chain-of-thought reasoning capabilities, with reinforcement learning (RL) playing a crucial role in this progress.
Lai Wei, Yuting Li, Kaipeng Zheng, Chen Wang, Yue Wang, Linghe Kong, Lichao Sun, Weiran Huang   +7 more
semanticscholar   +1 more source

Temporally and Distributionally Robust Optimization for Cold-start Recommendation

AAAI Conference on Artificial Intelligence, 2023
Collaborative Filtering (CF) recommender models highly depend on user-item interactions to learn CF representations, thus falling short of recommending cold-start items.
Xinyu Lin, Wenjie Wang, Jujia Zhao, Yongqi Li, Fuli Feng, Tat-seng Chua   +5 more
semanticscholar   +1 more source

Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap

arXiv.org
Cold-start problem is one of the long-standing challenges in recommender systems, focusing on accurately modeling new or interaction-limited users or items to provide better recommendations.
Weizhi Zhang, Yuan-Qi Bei, Liangwei Yang, Henry Peng Zou, Peilin Zhou, Aiwei Liu, Yinghui Li, Hao Chen, Jianling Wang, Yu Wang, Feiran Huang, Sheng Zhou, Jiajun Bu, Allen Lin, James Caverlee, F. Karray, Irwin King, Philip S. Yu   +17 more
semanticscholar   +1 more source

Cold-Start Next-Item Recommendation by User-Item Matching and Auto-Encoders

IEEE Transactions on Services Computing, 2023
Recommendation systems provide personalized service to users and aim at suggesting to them items that they may prefer. There is an increasing requirement of next-item recommendation systems to infer a user's next favor item based on his/her historical ...
Hanrui Wu, Chung Wang Wong, Jia Zhang, Yuguang Yan, Dahai Yu, J. Long, M. Ng   +6 more
semanticscholar   +1 more source

Cold Start Concept (CSC™): A Novel Catalyst for Cold Start Emission Control

SAE International Journal of Fuels and Lubricants, 2013
<div class="section abstract"><div class="htmlview paragraph">Catalytic emission control systems are installed on nearly all automobiles and heavy-duty trucks produced today to reduce exhaust emissions for the vehicles to meet government regulations.
Hai-Ying Chen, Shadab Mulla, Erich Weigert, Kenneth Camm, Todd Ballinger, Julian Cox, Phil Blakeman   +6 more
openaire   +1 more source

CMCLRec: Cross-modal Contrastive Learning for User Cold-start Sequential Recommendation

Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
Sequential recommendation models generate embeddings for items through the analysis of historical user-item interactions and utilize the acquired embeddings to predict user preferences.
Xiaolong Xu, Hongsheng Dong, Lianyong Qi, Xuyun Zhang, Haolong Xiang, Xiaoyu Xia, Yanwei Xu, Wan-Chun Dou   +7 more
semanticscholar   +1 more source

Elastic Feature Consolidation for Cold Start Exemplar-free Incremental Learning

International Conference on Learning Representations
Exemplar-Free Class Incremental Learning (EFCIL) aims to learn from a sequence of tasks without having access to previous task data. In this paper, we consider the challenging Cold Start scenario in which insufficient data is available in the first task ...
Simone Magistri, Tomaso Trinci, Albin Soutif-Cormerais, J. Weijer, Andrew D. Bagdanov   +4 more
semanticscholar   +1 more source

Generative Adversarial Framework for Cold-Start Item Recommendation

Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022
The cold-start problem has been a long-standing issue in recommendation. Embedding-based recommendation models provide recommendations by learning embeddings for each user and item from historical interactions.
Hao Chen, Zefan Wang, Feiran Huang, Xiao Huang, Yue Xu, Yishi Lin, Peng He, Zhoujun Li   +7 more
semanticscholar   +1 more source

Sustainable Serverless Computing With Cold-Start Optimization and Automatic Workflow Resource Scheduling

IEEE Transactions on Sustainable Computing
In recent years, serverless computing has garnered significant attention owing to its high scalability, pay-as-you-go billing model, and efficient resource management provided by cloud service providers.
Shanxing Pan, Hongyu Zhao, Zinuo Cai, Dongmei Li, Ruhui Ma, Haibing Guan   +5 more
semanticscholar   +1 more source