Abstract
This work addresses the problem of completing a partially filled matrix incorporating metadata associated with the rows and columns. The basic operation of matrix completion is modeled via deep matrix factorization, and the metadata associations are modeled as graphs. The problem is formally modeled as deep matrix factorization regularized by multiple graph Laplacians. The practical problem of collaborative filtering is an ideal candidate for the proposed solution. It needs to predict missing ratings between users and items, given demographic data of users and metadata associated with items. We show that the proposed solution improves over the state-of-the-art in collaborative filtering.
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Mongia, A., Jain, V., Majumdar, A. (2020). Deep Matrix Factorization on Graphs: Application to Collaborative Filtering. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_86
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DOI: https://doi.org/10.1007/978-3-030-63820-7_86
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