JISE


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Journal of Information Science and Engineering, Vol. 37 No. 2, pp. 281-293


 Building an Adaptive Recommendation Model Based on Fuzzy MP Neuron and Weighted Similarity Indicator


BAM BAHADUR SINHA AND DR. R. DHANALAKSHMI
Department of Computer Science and Engineering
National Institute of Technology Nagaland
Dimpaur, Nagaland, 797103 India
E-mail: {bahadurbam43; dhanalakshmiparthiban}@gmail.com


Recommender system is one of the most common data filtering techniques used. It helps to discover hidden patterns of information from a wide range of omnipresent products and services. When dataset drifts from scarcity to abundance, the most common methods such as collaborative filtering suffer from information sparsity complication, over-specification, and elevated computational complexity. We have created a hybrid model in this respect that considers between precision and computation time to produce the most appropriate products for customers in real time. We made use of imputation technique, fuzzy logic using novel similarity technique and McCulloch-Pitts (MP) Neuron to cope up with aforementioned complications. The experimental evaluation on MovieLens dataset and comparison with numerous state of art personalization models shows that the proposed model yields high efficiency and effectiveness. We tested the resultant classification accuracy of our proposed model using precision and recall.


Keywords: recommendation, fuzzy c-means, MP neuron, similarity, precision, recall

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