| 1. | Midhad Blazevic; Lennart B. Sina; Cristian A. Secco; Kawa Nazemi Recommendation of Scientific Publications—A Real-Time Text Analysis and Publication Recommendation System Journal Article In: Electronics, vol. 12, no. 7, 2023, ISSN: 2079-9292. @article{electronics12071699,
title = {Recommendation of Scientific Publications—A Real-Time Text Analysis and Publication Recommendation System},
author = {Midhad Blazevic and Lennart B. Sina and Cristian A. Secco and Kawa Nazemi},
url = {https://www.mdpi.com/2079-9292/12/7/1699},
doi = {10.3390/electronics12071699},
issn = {2079-9292},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Electronics},
volume = {12},
number = {7},
abstract = {Studies have shown that although having more information improves the quality of decision-making, information overload causes adverse effects on decision quality. Visual analytics and recommendation systems counter this adverse effect on decision-making. Accurately identifying relevant information can reduce the noise during exploration and improve decision-making. These countermeasures also help scientists make correct decisions during research. We present a novel and intuitive approach that supports real-time collaboration. In this paper, we instantiate our approach to scientific writing and propose a system that supports scientists. The proposed system analyzes text as it is being written and recommends similar publications based on the written text through similarity algorithms. By analyzing text as it is being written, it is possible to provide targeted real-time recommendations to improve decision-making during research by finding relevant publications that might not have been otherwise found in the initial research phase. This approach allows the recommendations to evolve throughout the writing process, as recommendations begin on a paragraph-based level and progress throughout the entire written text. This approach yields various possible use cases discussed in our work. Furthermore, the recommendations are presented in a visual analytics system to further improve scientists’ decision-making capabilities.},
keywords = {latex editor, publication recommendations, recommendation systems, similarity algorithms, topics modeling},
pubstate = {published},
tppubtype = {article}
}
Studies have shown that although having more information improves the quality of decision-making, information overload causes adverse effects on decision quality. Visual analytics and recommendation systems counter this adverse effect on decision-making. Accurately identifying relevant information can reduce the noise during exploration and improve decision-making. These countermeasures also help scientists make correct decisions during research. We present a novel and intuitive approach that supports real-time collaboration. In this paper, we instantiate our approach to scientific writing and propose a system that supports scientists. The proposed system analyzes text as it is being written and recommends similar publications based on the written text through similarity algorithms. By analyzing text as it is being written, it is possible to provide targeted real-time recommendations to improve decision-making during research by finding relevant publications that might not have been otherwise found in the initial research phase. This approach allows the recommendations to evolve throughout the writing process, as recommendations begin on a paragraph-based level and progress throughout the entire written text. This approach yields various possible use cases discussed in our work. Furthermore, the recommendations are presented in a visual analytics system to further improve scientists’ decision-making capabilities. |
