Recommender system using Long-term Cognitive Networks

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摘要

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.

论文关键词:Recommender system,Prior knowledge,Long-term Cognitive Networks

论文评审过程:Received 29 March 2020, Revised 2 August 2020, Accepted 3 August 2020, Available online 7 August 2020, Version of Record 10 August 2020.

论文官网地址:https://doi.org/10.1016/j.knosys.2020.106372