Recommender system using Long-term Cognitive Networks
Nápoles,Gonzalo Grau,Isel Salgueiro,Yamisleydi
Nápoles,Gonzalo
Grau,Isel
Salgueiro,Yamisleydi
Abstract
In this paper, we build a recommender system based on Long-term Cognitive Networks (LTCNs), which are a type of recurrent neural network that allows reasoning with prior knowledge structures. Given that our approach is context-free and that we did not involve human experts in our study, the prior knowledge is replaced with Pearson’s correlation coefficients. The proposed architecture expands the LTCN model by adding Gaussian kernel neurons that compute estimates for the missing ratings. These neurons feed the recurrent structure that corrects the estimates and makes the predictions. Moreover, we present an extension of the non-synaptic backpropagation algorithm to compute the proper non-linearity of each neuron together with its activation boundaries. Numerical results using several case studies have shown that our proposal outperforms most state-of-the-art methods. Towards the end, we explain how can we inject expert knowledge to the proposed neural system.
Description
Funding Information: The authors would like to thank the anonymous reviewers for their valuable and constructive feedback. This paper was partially supported by the Program FONDECYT de Postdoctorado, Chile through the project 3200284. Publisher Copyright: © 2020 Elsevier B.V.
Date
2020-10-28
Journal Title
Journal ISSN
Volume Title
Publisher
Research Projects
Organizational Units
Journal Issue
Keywords
Long-term Cognitive Networks, Prior knowledge, Recommender system
Citation
Nápoles, G, Grau, I & Salgueiro, Y 2020, 'Recommender system using Long-term Cognitive Networks', Knowledge-Based Systems, vol. 206, 106372. https://doi.org/10.1016/j.knosys.2020.106372