A Cooperative Coevolution Framework for Parallel Learning to Rank

• Main Authors: WANG, Shuaiqiang, WU, Yun, GAO, Byron J., WANG, Ke, LAUW, Hady W., MA, Jun
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2015
• Subjects: Cooperative coevolution; learning to rank; information retrieval; genetic programming; immune programming; Computer Sciences; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/2889; https://ink.library.smu.edu.sg/context/sis_research/article/3889/viewcontent/Lauw_2015_CCRank.pdf
• Institution: Singapore Management University

We propose CCRank, the first parallel framework for learning to rank based on evolutionary algorithms (EA), aiming to significantly improve learning efficiency while maintaining accuracy. CCRank is based on cooperative coevolution (CC), a divide-and-conquer framework that has demonstrated high promise in function optimization for problems with large search space and complex structures. Moreover, CC naturally allows parallelization of sub-solutions to the decomposed sub-problems, which can substantially boost learning efficiency. With CCRank, we investigate parallel CC in the context of learning to rank. We implement CCRank with three EA-based learning to rank algorithms for demonstration. Extensive experiments on benchmark datasets in comparison with the state-of-the-art algorithms show the performance gains of CCRank in efficiency and accuracy.