Iterative Refinement Methods for Enhanced Information Retrieval

doi:10.1002/int.21641

2014-01-10

DOI：10.1002/int.21641

原文链接

查看原文

摘要

En 中文

Information retrieval (IR) systems exploit relevant information when tailoring search results to individual information needs. However, current search experience becomes poor without considering similar queries entered by previous searchers. In the following paper, we discuss a solution to this problem, which combines collaborative filtering algorithms with traditional IR models to enable EIR. We also present various iterative refinement methods for improving the raw performance of this system. We validate our theories in an experiment using queries extracted from the click-through log of a commercial search engine. According to our results, an IR system employing iteratively refined, collaborative retrieval significantly outperforms various baseline retrieval models. (C) 2014 Wiley Periodicals, Inc.

Keyword:

MODELS

AI总结

对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

International Journal of Intelligent Systems

IF:

3.7

论文数:

3.1K

被引数:

8.1K

机构

university of teesside

学者数:

1.6K

论文数: 1.8K

被引数: 3

引用论文

Transplantation in Asia during the coronavirus disease-19 (COVID-19) pandemic: briefs from member countries of the Asian Society of Transplantation

Korean Journal of Transplantation

IF0

2020-06-30

OAAI

Terence Kee; Jong Cheol Jeong; Jongwon Ha; Harun Ur Rashid; Nura Afza Salma Begum; Maggie K.M. Ma; Vivek Kute; Kenji Yuzawa; Rosnawati Yahya; Ghazali Ahmad; Lkhaakhuu Od-Erdene; Dibya Singh Shah; Romina A Danguilan; Curie Ahn; Devinder Singh Rana

Synthesis of complement by macrophages and modulation of their functions through complement activation

Springer Seminars in Immunopathology

IF0

1983-12-01

PREAI

H. P. Hartung; U. Hadding

Molecular interaction of NMDA receptor subunit NR3A with protein phosphatase 2A

NeuroReport

IF0

2004-06-28

PREAI

On Ki Ma; Nikolaus J. Sucher