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.

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SIMT: A Semantic Interest Modeling Toolkit | Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization

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