Application of Recommended Systems for E-commerce
Gulnara Bektemyssova, Shyntore Guldana, Akhmer Yerassyl
Abstract
A filtering method is indispensable in a data-flooded environment. Recommended systems have made a massive step towards this aim, speeding up internet-based customer experience. Most of today's examples of artificial marketing intelligence are known as supervised learning, which varies from offering personalized specific products identifying the most valuable marketing strategies, to forecasting customer churn rate or customer life value, and building up a positive client base. Generally, different types of stored information are used to customize various dimensions or search results, demonstrate the most targeted advertising on the homepage, etc. n our research we have also incorporated the benefits and drawbacks of every approach. Finally, this paper also presents numerous difficulties and problems confronting recommenders in their application systems algorithms. In this paper, we initially present multiple best-known types of recommended systems and concentrate on one part of the e- commerce recommendation and afterwards make their quantitative comparison. Recommender systems have taken a huge step towards this goal, greatly improving the user experience in the online environment.