Bias-Aware Recommender System using Topic Modeling


Bias-Aware Recommender System using Topic Modeling – In this paper we propose the first framework for hierarchical recommender system to learn topic models in an online manner. To this end, we present a Bayesian recommender system based on Topic Modeling. The proposed approach allows us to learn topic models that are more relevant to the users. The model for recommendation is given as an example, and the user is asked to perform some action or reward in order to learn more topic models. The topic model is provided using Topic Modeling. The proposed algorithms can be considered as a reinforcement learning technique, which can be used to optimize the performance of the recommender system.

In this paper, we aim at enhancing students’ academic success through strategic search and collaborative learning. We consider the problem of assessing how students’ academic performance compares to how their parents or teachers grade scores: for each student, we aim to identify a sequence of grades, which in turn determines how much score they should attain. The resulting system is trained on a large-scale dataset collected from a social network, which we use to evaluate the performance of students. We demonstrate that the predictive ranking of the students improves with the number of grades, which increases exponentially after being aggregated together. Based on a simple and robust evaluation system, we present and evaluate several strategic search systems. Our system achieves an overall improvement of ~12.8% on average when compared to a state-of-the-arts system evaluated from the beginning, which only achieves an average ~10.2% improvement when compared to a teacher who only requires ~8.2% in grades.

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Bias-Aware Recommender System using Topic Modeling

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  • A study of social network statistics and sentiment

    Improving Students’ Academic Success Through Strategic Search and Interactive LearningIn this paper, we aim at enhancing students’ academic success through strategic search and collaborative learning. We consider the problem of assessing how students’ academic performance compares to how their parents or teachers grade scores: for each student, we aim to identify a sequence of grades, which in turn determines how much score they should attain. The resulting system is trained on a large-scale dataset collected from a social network, which we use to evaluate the performance of students. We demonstrate that the predictive ranking of the students improves with the number of grades, which increases exponentially after being aggregated together. Based on a simple and robust evaluation system, we present and evaluate several strategic search systems. Our system achieves an overall improvement of ~12.8% on average when compared to a state-of-the-arts system evaluated from the beginning, which only achieves an average ~10.2% improvement when compared to a teacher who only requires ~8.2% in grades.


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