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Neural Cross-Domain Collaborative Filtering with Shared Entities

Vijaikumar, M and Shevade, S and Murty, MN (2021) Neural Cross-Domain Collaborative Filtering with Shared Entities. In: European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, 14-18 Sep 2020, pp. 729-745.

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Official URL: https://doi.org/10.1007/978-3-030-67658-2_42


Cross-Domain Collaborative Filtering (CDCF) provides a way to alleviate data sparsity and cold-start problems present in recommendation systems by exploiting the knowledge from related domains. Existing CDCF models are either based on matrix factorization or deep neural networks. Independent use of either of the techniques in isolation may result in suboptimal performance for the prediction task. Also, most of the existing models face challenges particularly in handling diversity between domains and learning complex non-linear relationships that exist amongst entities (users/items) within and across domains. In this work, we propose an end-to-end neural network model � NeuCDCF, to address these challenges in a cross-domain setting. More importantly, NeuCDCF is based on a wide and deep framework and learns the representations jointly using both matrix factorization and deep neural networks. We perform experiments on four real-world datasets and demonstrate that our model performs better than state-of-the-art CDCF models. © 2021, Springer Nature Switzerland AG.

Item Type: Conference Paper
Publication: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher: Springer Science and Business Media Deutschland GmbH
Additional Information: The copyright for this article belongs to Springer Science and Business Media Deutschland GmbH
Keywords: Collaborative filtering; Data mining; Deep learning; Deep neural networks; Factorization; Learning systems; Matrix algebra, Cold start problems; Handling diversity; Matrix factorizations; Neural network model; Non-linear relationships; Prediction tasks; Real-world datasets; Sub-optimal performance, Neural networks
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 09 Jul 2021 06:53
Last Modified: 09 Jul 2021 06:53
URI: http://eprints.iisc.ac.in/id/eprint/68741

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