Research on B Cell Algorithm for Learning to Rank Method Based on Parallel Strategy

Yuling Tian and Hongxian Zhang

PLOS ONE, 2016, vol. 11, issue 8, 1-20

Abstract: For the purposes of information retrieval, users must find highly relevant documents from within a system (and often a quite large one comprised of many individual documents) based on input query. Ranking the documents according to their relevance within the system to meet user needs is a challenging endeavor, and a hot research topic–there already exist several rank-learning methods based on machine learning techniques which can generate ranking functions automatically. This paper proposes a parallel B cell algorithm, RankBCA, for rank learning which utilizes a clonal selection mechanism based on biological immunity. The novel algorithm is compared with traditional rank-learning algorithms through experimentation and shown to outperform the others in respect to accuracy, learning time, and convergence rate; taken together, the experimental results show that the proposed algorithm indeed effectively and rapidly identifies optimal ranking functions.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0157994

DOI: 10.1371/journal.pone.0157994

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