Abstract
In the domain of recommendation, the fusion of user historical preferences and item features has emerged as a promising field to enhance recommendation accuracy and mitigate the sparsity problem. This research presents a novel approach to recommendation systems by integrating both user and item content auxilary information. By harnessing the rich contextual data associated with users and items, our hybrid aproach ConvSeq-MF endeavors to provide more personalized and accurate recommendations. As the name indivates, it is based on a matrix factorization approach driven by joint venture of semantic and contextual information of both user’s and item’s. Series of experimentation on three real-world datasets shows the suggested methodology’s practicality and efficacy over otther baselines. To the best of our knowledge, the data demonstrate that the suggested model works better than other state of the art frameworks.
Published Version
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