In this paper, we focus on semantic interest modeling and present SIMT as a toolkit that harnesses the semantic information to effectively generate user interest models and compute their similarities. SIMT follows a mixed-method approach that combines unsupervised keyword extraction algorithms, knowledge bases, and word embedding techniques to address the semantic issues in the interest modeling task.
Description
SIMT: A Semantic Interest Modeling Toolkit | Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization
%0 Conference Paper
%1 2021
%A Chatti, Mohamed Amine
%A Ji, Fangzheng
%A Guesmi, Mouadh
%A Muslim, Arham
%A Singh, Ravi Kumar
%A Joarder, Shoeb Ahmed
%D 2021
%I ACM
%K umap2021 user-modeling wikipedia
%P 75-78
%R 10.1145/3450614.3461676
%T SIMT: A Semantic Interest Modeling Toolkit
%U https://doi.org/10.1145%2F3450614.3461676
%X In this paper, we focus on semantic interest modeling and present SIMT as a toolkit that harnesses the semantic information to effectively generate user interest models and compute their similarities. SIMT follows a mixed-method approach that combines unsupervised keyword extraction algorithms, knowledge bases, and word embedding techniques to address the semantic issues in the interest modeling task.
@inproceedings{2021,
abstract = {In this paper, we focus on semantic interest modeling and present SIMT as a toolkit that harnesses the semantic information to effectively generate user interest models and compute their similarities. SIMT follows a mixed-method approach that combines unsupervised keyword extraction algorithms, knowledge bases, and word embedding techniques to address the semantic issues in the interest modeling task.
},
added-at = {2021-10-13T23:27:52.000+0200},
author = {Chatti, Mohamed Amine and Ji, Fangzheng and Guesmi, Mouadh and Muslim, Arham and Singh, Ravi Kumar and Joarder, Shoeb Ahmed},
biburl = {https://www.bibsonomy.org/bibtex/2d406de33ae4a027f4fb97d3bfaa2e836/brusilovsky},
description = {SIMT: A Semantic Interest Modeling Toolkit | Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization},
doi = {10.1145/3450614.3461676},
interhash = {b62906bb09ee2f9b051a592c9aa4c8d1},
intrahash = {d406de33ae4a027f4fb97d3bfaa2e836},
keywords = {umap2021 user-modeling wikipedia},
month = jun,
pages = {75-78},
publisher = {{ACM}},
timestamp = {2021-10-13T23:27:52.000+0200},
title = {{SIMT}: A Semantic Interest Modeling Toolkit},
url = {https://doi.org/10.1145%2F3450614.3461676},
year = 2021
}