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Semantic Product Ranking Model (SePRaM) using PNN over the Heuristic Product Data
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 146 - Number 7 |
| Year of Publication: 2016 |
| Authors: Gurkiran Kaur, Rekha Bhatia |
10.5120/ijca2016910888
|
Gurkiran Kaur, Rekha Bhatia . Semantic Product Ranking Model (SePRaM) using PNN over the Heuristic Product Data. International Journal of Computer Applications. 146, 7 ( Jul 2016), 36-40. DOI=10.5120/ijca2016910888
Abstract
The product ranking models for the e-commerce engines are primarily based upon the various types of product ranking engines based upon collaborative, content-based, hybrid, ordinal, partial and dense ranking models for the realization of the e-commerce product ranking module. The proposed model is based upon the hybridized approach, which is based upon the dual-stage rank preparation. The first stage rank preparation is entirely based upon the content-based ranking model, which evaluates the similarity between the search query arguments and the product ranking data. The product ranking data is prepared by using the various factors associated with the product popularity and accessibility against the search query arguments. The product suggestions are calculated to show the product rankings on the search page to the users. Once the user browsed the specific product, the collaborative classification is used for the higher order product suggestions on the product page. The collaborative approach analyzes the user similarity and produces the product rank lists according to the top listed users in the ranking evaluation. The proposed model evaluation has been analyzed in the form of various time based factors to read the time complexity over the input product data. The proposed model has outperformed the existing models in the terms of the precision and elapsed time.
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