Results 201 to 210 of about 250,954 (220)

Information Retrieval Ranking Using Machine Learning Techniques

2019 Amity International Conference on Artificial Intelligence (AICAI), 2019
Information retrieval is the research area in which many researcher have been done and many are still going on. The rapidly growing web pages make it very crucial to search up to date documents. In continuation of research works on learning to rank, this research focuses on implication of machine learning techniques for IR ranking.
Shweta Pandey, Iti Mathur, Nisheeth Joshi   +2 more
openaire   +1 more source

Machine Learning Ranking for Structured Information Retrieval

2006
We consider the Structured Information Retrieval task which consists in ranking nested textual units according to their relevance for a given query, in a collection of structured documents. We propose to improve the performance of a baseline Information Retrieval system by using a learning ranking algorithm which operates on scores computed from ...
Vittaut, Jean-Noël, Gallinari, Patrick
openaire   +1 more source

Learning to rank for biomedical information retrieval

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2015
Research articles in biomedicine domain have increased exponentially, which makes it more and more difficult for biologists to manually capture all the information they need. Information retrieval technologies can help to obtain the users' needed information automatically.
null Bo Xu, null Hongfei Lin, null Yuan Lin, null Yunlong Ma, null Liang Yang, null Jian Wang, null Zhihao Yang   +6 more
openaire   +1 more source

Efficient Information Retrieval of Encrypted Cloud Data with Ranked Retrieval

2019
Over the past decade, there have been massive developments in technology such as self-driving cars, crypto currencies, streaming services, voice assistants etc. In each of the listed breakthroughs, Cloud Computing was involved. Cloud computing has offered a tremendous breakthrough in enterprise and business transformation bringing with it a previously ...
Arun Syriac, V. Anjana Devi, M. Gogul Kumar   +2 more
openaire   +1 more source

Improving Information Retrieval by Concept-Based Ranking

2006
With the Internet getting available to more and more people in the last decade and with the rapidly growing number of webpages the internet is a vast resource of information. Millions of people are using the internet to search for information every day. However, the search result is not satisfying in the case of an ambiguous search query. In this paper
Martin Mehlitz, Fang Li
openaire   +1 more source

Ranking refinement and its application to information retrieval

Proceedings of the 17th international conference on World Wide Web, 2008
We consider the problem of ranking refinement, i.e., to improve the accuracy of an existing ranking function with a small set of labeled instances. We are, particularly, interested in learning a better ranking function using two complementary sources of information, ranking information given by the existing ranking function (i.e., a base ranker) and ...
Rong Jin, Hamed Valizadegan, Hang Li
openaire   +1 more source

Maximum Margin Ranking Algorithms for Information Retrieval

2010
Machine learning ranking methods are increasingly applied to ranking tasks in information retrieval (IR). However ranking tasks in IR often differ from standard ranking tasks in machine learning, both in terms of problem structure and in terms of the evaluation criteria used to measure performance.
Shivani Agarwal, Michael Collins
openaire   +1 more source

Weighted Rank Correlation in Information Retrieval Evaluation

2009
In Information Retrieval (IR), it is common practice to compare the rankings observed during an experiment --- the statistical procedure to compare rankings is called rank correlation. Rank correlation helps decide the success of new systems, models and techniques. To measure rank correlation, the most used coefficient is Kendall's *** . However, in IR,
openaire   +2 more sources

Efficient cascade ranking for information retrieval

Web search services play a central role in modern society, providing access to information and knowledge. Relevance ranking for web search is a highly imbalanced problem, where non-relevant documents far out number those that are relevant to a user's particular information need.
openaire   +1 more source

Methods for ranking information retrieval systems without relevance judgments

ACM Symposium on Applied Computing, 2003
Shengli Wu, F. Crestani
semanticscholar   +1 more source