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Recommender system using Long-term Cognitive Networks
- Source :
- Knowledge-Based Systems, 206:106372. Elsevier
- Publication Year :
- 2020
-
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. 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.
- Subjects :
- Information Systems and Management
Computer science
Long-term Cognitive Networks
02 engineering and technology
Recommender system
Machine learning
computer.software_genre
Management Information Systems
symbols.namesake
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
Gaussian function
Structure (mathematical logic)
business.industry
Extension (predicate logic)
Cognitive network
artificial intelligence
Backpropagation
Term (time)
Prior knowledge
Recurrent neural network
symbols
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
Software
Subjects
Details
- Language :
- English
- ISSN :
- 09507051
- Volume :
- 206
- Database :
- OpenAIRE
- Journal :
- Knowledge-Based Systems
- Accession number :
- edsair.doi.dedup.....6398f244e9c9c988e23e4fcb74e97908