SIMT: A Semantic Interest Modeling Toolkit
Mohamed Amine Chatti, Fangzheng Ji, Mouadh Guesmi, Arham Muslim, Ravi Kumar Singh, Shoeb Joarder · 2021
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.