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.

We propose a novel, principled strategy of learning sparse representations that is well suited in domains with large amounts of labels and features. We also develop a learning algorithm that can be generalized to sparse representations and then to any sparse representation. We also design and evaluate a large-scale learning algorithm for learning sparse representations without the need to explicitly label them. We demonstrate the power of the proposed algorithm on large-scale and distributed dataset of image classification problems with labeled and unlabeled data.

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Bias-Aware Recommender System using Topic Modeling

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  • Hessian Distance Regularization via Nonconvex Sparse Estimation

    An Adaptive Classification Method for Learning Sparse RepresentationsWe propose a novel, principled strategy of learning sparse representations that is well suited in domains with large amounts of labels and features. We also develop a learning algorithm that can be generalized to sparse representations and then to any sparse representation. We also design and evaluate a large-scale learning algorithm for learning sparse representations without the need to explicitly label them. We demonstrate the power of the proposed algorithm on large-scale and distributed dataset of image classification problems with labeled and unlabeled data.


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