Collaborative online ranking algorithms for multitask learning

• Main Authors: LI, Guangxia, ZHAO, Peilin, MEI, Tao, YANG, Peng, SHEN, Yulong, CHANG, Julian K. Y., HOI, Steven C. H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Learning to rank; Online learning; Multitask learning; Databases and Information Systems; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/5131; https://ink.library.smu.edu.sg/context/sis_research/article/6134/viewcontent/Coll_online_ranking_algor_multitask_av.pdf
• Institution: Singapore Management University

There are many applications in which it is desirable to rank or order instances that belong to several different but related problems or tasks. Although unique, the individual ranking problem often shares characteristics with other problems in the group. Conventional ranking methods treat each task...