NDLI: An adaptive learning automata-based ranking function discovery algorithm

Content Provider	Springer Nature Link
Author	Akbari Torkestani, Javad
Copyright Year	2012
Abstract	Due to the massive amount of heterogeneous information on the web, insufficient and vague user queries, and use of the same query by different users for different aims, the information retrieval process deals with a huge amount of uncertainty and doubt. Under such circumstances, designing an efficient retrieval function and ranking algorithm by which the most relevant results are provided is of the greatest importance. In this paper, a learning automata-based ranking function discovery algorithm in which different sources of information are combined is proposed. In this method, the learning automaton is used to adjust the portion of the final ranking that is assigned to each source of evidence based on the user feedback. All sources of information are first given the same importance. The proportion of a given source increases, if the documents provided by this source are reviewed by the user and decreases otherwise. As the proposed algorithm proceeds, the probability of appearance of each source in the final ranking gets proportional to its relevance to the user queries. Several simulation experiments are conducted on well-known data collections and query types to show the performance of the proposed algorithm. The obtained results demonstrate that the proposed algorithm outperforms several existing methods in terms of precision at position n, mean average precision, and normalized discount cumulative gain.
Starting Page	441
Ending Page	459
Page Count	19
File Format	PDF
ISSN	09259902
Journal	Journal of Intelligent Information Systems
Volume Number	39
Issue Number	2
e-ISSN	15737675
Language	English
Publisher	Springer US
Publisher Date	2012-03-01
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Information retrieval Ranking function Search engine Learning automata Artificial Intelligence (incl. Robotics) Business Information Systems Information Storage and Retrieval Document Preparation and Text Processing Data Structures, Cryptology and Information Theory
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Information Systems Software Hardware and Architecture

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