A generic ranking function discovery framework by genetic programming for information retrieval

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@Article{Fan2003b,

• author = "Weiguo Fan and Michael D. Gordon and Praveen Pathak",
• title = "A generic ranking function discovery framework by genetic programming for information retrieval",
• journal = "Information Processing and Management",
• year = "2003",
• volume = "40",
• number = "4",
• pages = "587--602",
• keywords = "genetic algorithms, genetic programming, Information retrieval; Ranking function, Text mining",
• DOI = "doi:10.1016/j.ipm.2003.08.001",
• URL = "http://filebox.vt.edu/users/wfan/paper/ARRANGER/ip&m2003.pdf",
• URL = "http://www.sciencedirect.com/science/article/B6VC8-49J8S58-2/2/158a3713b59ef9defad7d00e81707f66",
• size = "16 pages",
• abstract = "Ranking functions play a substantial role in the performance of information retrieval (IR) systems and search engines. Although there are many ranking functions available in the IR literature, various empirical evaluation studies show that ranking functions do not perform consistently well across different contexts (queries, collections, users). Moreover, it is often difficult and very expensive for human beings to design optimal ranking functions that work well in all these contexts. In this paper, we propose a novel ranking function discovery framework based on Genetic Programming and show through various experiments how this new framework helps automate the ranking function design/discovery process.",

}

Genetic Programming entries for Weiguo Fan Michael D Gordon Praveen Pathak

Citations