Topic relevance and diversity in information retrieval from large datasets: A multi-objective evolutionary algorithm approach-Reference-Cited by-同舟云学术

Topic relevance and diversity in information retrieval from large datasets: A multi-objective evolutionary algorithm approach

Published:2018-08 Issue: Volume:69 Page:749-770
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Cecchini Rocío L.,Lorenzetti Carlos M.^ORCID,Maguitman Ana G.,Ponzoni Ignacio

Publisher

Elsevier BV

Subject

Software

Reference90 articles.

1. Using query context models to construct topical search engines;Sondhi,2010

2. R. Chandrasekar, P. Sondhi, R. Rounthwaite, R. Chandrasekar, P. Sondhi, R. Rounthwaite, Topical search engines and query context models, uS Patent 9,177,045. (03.11.15).

3. Experience-based support for human-centered knowledge modeling;Leake;Knowl. Based Syst.,2014

4. User modeling for a personal assistant;Guha,2015

5. Learning to crawl deep web;Zheng;Inf. Syst.,2013

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Multi-objective genetic programming strategies for topic-based search with a focus on diversity and global recall;PeerJ Computer Science;2023-11-30

2. Study of Evolutionary Algorithms for Multi-objective Optimization;SN Computer Science;2022-08-02

3. Matching Search Result Diversity with User Diversity Acceptance in Web Search Sessions;Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval;2022-07-06

4. Knowledge-based framework for estimating the relevance of scientific articles;Expert Systems with Applications;2020-12

5. Multi-Objective GP Strategies for Topical Search Integrating Wikipedia Concepts;Proceedings of the ACM Symposium on Document Engineering 2019;2019-09-23