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

Cecchini, Rocío L.; Lorenzetti, Carlos M.; Maguitman, Ana Gabriela; Ponzoni, Ignacio

doi:10.1016/j.asoc.2017.11.016

Cited by 11 publications

(1 citation statement)

References 78 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They demonstrate that their system has a better ability to find relevant documents by examining diverse meanings for the query. Cecchini, et al (2018) also look at search diversification and use a genetic algorithm to evolve a population of queries to improve coverage and relevance of search results.…”

Section: Related Workmentioning

confidence: 99%

A Genetic Algorithm for Finding a Small and Diverse Set of Recent News Stories on a Given Subject: How We Generate AAAI’s AI-Alert

Eckroth

Schoen

2019

AAAI

View full text Add to dashboard Cite

This paper describes the genetic algorithm used to select news stories about artificial intelligence for AAAI’s weekly AIAlert, emailed to nearly 11,000 subscribers. Each week, about 1,500 news stories covering various aspects of artificial intelligence and machine learning are discovered by i2k Connect’s NewsFinder agent. Our challenge is to select just 10 stories from this collection that represent the important news about AI. Since stories and topics do not necessarily repeat in later weeks, we cannot use click tracking and supervised learning to predict which stories or topics are most preferred by readers. Instead, we must build a representative selection of stories a priori, using information about each story’s topics, content, publisher, date of publication, and other features. This paper describes a genetic algorithm that achieves this task. We demonstrate its effectiveness by comparing several engagement metrics from six months of “A/B testing” experiments that compare random story selection vs. a simple scoring algorithm vs. our new genetic algorithm.

show abstract

Section: Related Workmentioning

confidence: 99%