Learning to Rank for Information Retrieval Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Yeh:2007:SIGIR,

• author = "Jen-Yuan Yeh and Jung-Yi Lin and Hao-Ren Ke and Wei-Pang Yang",
• title = "Learning to Rank for Information Retrieval Using Genetic Programming",
• booktitle = "SIGIR 2007 workshop: Learning to Rank for Information Retrieval",
• year = "2007",
• editor = "Thorsten Joachims and Hang Li and Tie-Yan Liu and ChengXiang Zhai",
• month = "27 " # jul,
• organisation = "Microsoft",
• keywords = "genetic algorithms, genetic programming, learning to rank for IR, ranking function, Information Storage and Retrieval, Information Search and Retrieval, Retrieval Models, Algorithms, Experimentation, Performance",
• URL = "http://jenyuan.yeh.googlepages.com/jyyeh-LR4IR07.pdf",
• size = "8 pages",
• abstract = "One central problem of information retrieval (IR) is to determine which documents are relevant and which are not to the user information need. This problem is practically handled by a ranking function which defines an ordering among documents according to their degree of relevance to the user query. This paper discusses work on using machine learning to automatically generate an effective ranking function for IR. This task is referred to as learning to rank for IR in the field. In this paper, a learning method, RankGP, is presented to address this task. RankGP employs genetic programming to learn a ranking function by combining various types of evidences in IR, including content features, structure features, and query-independent features. The proposed method is evaluated using the LETOR benchmark datasets and found to be competitive with Ranking SVM and RankBoost.",
• notes = "broken Jun 2023 https://research.microsoft.com/en-us/um/beijing/events/LR4IR-2007/",

}

Genetic Programming entries for Jen-Yuan Yeh Mick Jung-Yi Lin Hao-Ren Ke Wei-Pang Yang

Citations