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An automatic product recommendation system in e-commerce using Flamingo Search Optimizer and Fuzzy Temporal Multi Neural Classifier

B. Manikandan, P. Rama, S. Chakaravarthi

Abstract


In  this  paper,  a  new  automatic  product  recommendation  system  (APRS)  is  proposed  to  recommend  the  suitable products  to  the  customer  in  e-commerce by analyzing the customers’ reviews. This recommendation system applies semantic   aware   data   preprocessing,   feature   selection   and   extraction   and   classification.   The   initial   level   data preprocessing including blank space and stop word removal. Moreover, we use a Flamingo Search Optimizer (FSO) for optimizing  the  features  that  are  extracted  in  the  initial  level  data  preprocessing.  In  addition,  a  new  Fuzzy  Temporal Multi Neural Classification Algorithm (FTMNCA) is proposed for performing effective classification that is helpful to make  effective  decision  on  prediction  process.  In  addition,  the  proposed  automatic  productrecommendation  system recommends the suitable products to the customers according to the classification result. Finally, the proposed system is evaluated by conducting various experiments and proved as superior than the available systems in terms of prediction accuracy, precision, recall and f-measure.

Keywords


automatic product recommendation system; flamingo search optimizer; Multi Neural Classifier; fuzzy logic and temporal constraints

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References


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DOI: https://doi.org/10.32629/jai.v6i2.568

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