%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 }