Shuaiqiang Wang
Jun Ma
ABSTRACT
Nowadays ranking function discovery approaches using Evolutionary Computation (EC), especially Genetic Programming (GP), have become an important branch in the Learning to Rank for Information Retrieval (LR4IR) field. Inspired by the GP based learning to rank approaches, we provide a series of generalized definitions and a common framework for the application of EC in learning to rank research. Besides, according to the introduced framework, we propose RankIP, a ranking function discovery approach using Immune Programming (IP). Experimental results demonstrate that RankIP evidently outperforms the baselines.
In addition, we study the differences between IP and GP in theory and experiments. Results show that IP is more suitable for LR4IR due to its high diversity.
- L. Bai, M. Eyiyurekli, and D. E. Breen. Automated shape composition based on cell biology and distributed genetic programming. In Proceedings of GECCO'08, pages 1179--1186, 2008. Google Scholar
Digital Library
- H. M. de Almeida,M. A. Gonşalves, M. Cristo, and P. Calado. A combined component approach for finding collection-adapted ranking functions based on genetic programming. In Proceedings of SIGIR'07, pages 399--406, 2007. Google ScholarDigital Library
- W. Fan, M. D. Gordon, and P. Pathak. Discovery of context-specific ranking functions for effective information retrieval using genetic programming. IEEE Transactions on Knowledge and Data Engineering, 16(4): 523--527, 2004. Google ScholarDigital Library
- Y. Freund, R. Iyer, R. E. Schapire, and Y. Singer. An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research, 4: 933--969, 2003. Google ScholarDigital Library
- T. Joachims. Optimizing search engines using clickthrough data. In roceedings of KDD'02, pages 133--142. ACM, 2002. Google ScholarDigital Library
- T.-Y. Liu, J. Xu, T. Qin, W. Xiong, and H. Li. LETOR: Benchmark dataset for research on learning to rank for information retrieval. In SIGIR Workshop on Learning to Rank for IR (LR4IR), July 2007.Google Scholar
- P. Musilek, A. Lau, M. Reformat, and L. Wyard-Scott. Immune programming. Information Sciences, 176(8): 972--1002, 2006. Google ScholarDigital Library
- S. E. Robertson. Overview of the okapi projects. Journal of Documentation, 53(1): 3--7, 1997.Google ScholarCross Ref
- A. Trotman. Learning to rank. Information Retrieval, 8(3): 359--381, 2005. Google ScholarDigital Library
Index Terms
- Learning to rank using evolutionary computation: immune programming or genetic programming?
Recommendations
An immune programming-based ranking function discovery approach for effective information retrieval
In this paper, we propose RankIP, the first immune programming (IP) based ranking function discovery approach. IP is a novel evolution based machine learning algorithm with the principles of immune systems, which is verified to be superior to Genetic ...
Evaluating Google queries based on language preferences
This paper evaluates the assumption that users expect search engines to retrieve the same results for queries regardless of the language or the location of the originator. The dependency of the Google search engine on the language and location from ...
On Application of Learning to Rank for E-Commerce Search
SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information RetrievalE-Commerce (E-Com) search is an emerging important new application of information retrieval. Learning to Rank (LETOR) is a general effective strategy for optimizing search engines, and is thus also a key technology for E-Com search. While the use of ...
Comments