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Full-Text Articles in Physical Sciences and Mathematics
A Cooperative Coevolution Framework For Parallel Learning To Rank, Shuaiqiang Wang, Yun Wu, Byron J. Gao, Ke Wang, Hady W. Lauw, Jun Ma
A Cooperative Coevolution Framework For Parallel Learning To Rank, Shuaiqiang Wang, Yun Wu, Byron J. Gao, Ke Wang, Hady W. Lauw, Jun Ma
Research Collection School Of Computing and Information Systems
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 …
Social Tag Relevance Estimation Via Ranking-Oriented Neighbour Voting, Chaoran Cui, Jialie Shen, Jun Ma, Tao Lian
Social Tag Relevance Estimation Via Ranking-Oriented Neighbour Voting, Chaoran Cui, Jialie Shen, Jun Ma, Tao Lian
Research Collection School Of Computing and Information Systems
User-generated tags associated with social images are frequently imprecise and incomplete. Therefore, a fundamental challenge in tag-based applications is the problem of tag relevance estimation, which concerns how to interpret and quantify the relevance of a tag with respect to the contents of an image. In this paper, we address the key problem from a new perspective of learning to rank, and develop a novel approach to facilitate tag relevance estimation to directly optimize the ranking performance of tag-based image search. A supervision step is introduced into the neighbour voting scheme, in which tag relevance is estimated by accumulating votes …