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A New Weighted-learning Approach for Exploiting Data Sparsity in Tag-based Item Recommendation Systems
2021
International Journal of Intelligent Engineering and Systems
The tag-based recommendation systems that are built based on tensor models commonly suffer from the data sparsity problem. In recent years, various weighted-learning approaches have been proposed to tackle such a problem. The approaches can be categorized by how a weighting scheme is used for exploiting the data sparsity – like employing it to construct a weighted tensor used for weighing the tensor model during the learning process. In this paper, we propose a new weighted-learning approach
doi:10.22266/ijies2021.0228.36
fatcat:vfoqdqj4pfcmhm3glp4zrzuthm