%0 Conference Proceedings
%T Online Product Recommendation System Using Multi Scenario Demographic Hybrid (MDH) Approach
%+ Vellore Institute of Technology (VIT)
%A Karthik, R., V.
%A Ganapathy, Sannasi
%Z Part 3: Data Science
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 3rd International Conference on Computational Intelligence in Data Science (ICCIDS)
%C Chennai, India
%Y Aravindan Chandrabose
%Y Ulrich Furbach
%Y Ashish Ghosh
%Y Anand Kumar M.
%I Springer International Publishing
%3 Computational Intelligence in Data Science
%V AICT-578
%P 248-260
%8 2020-02-20
%D 2020
%R 10.1007/978-3-030-63467-4_20
%K Ecommerce
%K User reviews
%K Recommendation
%Z Computer Science [cs]Conference papers
%X The recommendation system plays very important role in the ecommerce domain to recommend the relevant products/services based on end user preference or interest. This helps end users to easily make the buying decision from a vast range of product and brands are available in the market. A lot of research is done in recommendation system, aim to provide the relevant product to the end user by referring end user past purchase history, transaction details etc. In our Multi scenario demographic hybrid (MDH) approach, important demographic influence factors like the user age group and located area are considered. The products are also ranked with associated age group category. The experimental results of the proposed recommendation system have proven that it is better than the existing systems in terms of prediction accuracy of relevant products.
%G English
%Z TC 12
%2 https://inria.hal.science/hal-03434788/document
%2 https://inria.hal.science/hal-03434788/file/507484_1_En_20_Chapter.pdf
%L hal-03434788
%U https://inria.hal.science/hal-03434788
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ GENCI
%~ IFIP-TC12
%~ IFIP-ICCIDS
%~ IFIP-AICT-578