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Provisioning a cross-domain recommender system using an adaptive adversarial network model

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Abstract

Recommender system (RS) aims to predict user preferences based on automatic data acquisition, and those collected data assist in achieving the final decision. However, RS suffers from data sparsity issues over the newly launched system, and the lack of time to deal with the massive data is also a challenging factor. To acquire proper outcomes, cross-domain RS intends to transfer knowledge from the specific domain with quality enriched data to help recommendations to the target domains. The entities may or may not be overlapped, and it is common for the entities of two domains to be overlapped. These overlapping entities may show variations in their target domain, and avoiding these issues leads to distorted prediction outcomes over the cross-domain RS. To address these issues, this research concentrates on modeling and efficient cross-domain RS using the generative and discriminative adversarial network (CRS-GDAN) model for kernel-based transfer modeling. Domain specific is considered to handle the feature space of overlapped entities, and transfer computation is adopted to handle the overlapping and non-overlapping entity correlation among the domains. Based on the anticipated concept, knowledge transfer is achieved rigorously even in the case of overlapping entities, thus diminishing the data sparsity issues. The experimentation is performed using an available online dataset, and the model attains a 20% better outcome than other approaches. The outcomes specify that the knowledge transfer from source to destination target is advantageous even in overlapping issues.

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Authors and Affiliations

  1. Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India

    M. Nanthini & K. Pradeep Mohan Kumar

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  1. M. Nanthini
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  2. K. Pradeep Mohan Kumar
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Nanthini, M., Kumar, K.P.M. Provisioning a cross-domain recommender system using an adaptive adversarial network model. Soft Comput 27, 19197–19212 (2023). https://doi.org/10.1007/s00500-023-09360-w

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