基于机器学习推荐系统的电子商务平台

Muhammad Tahir, Rabia Noor Enam, Syed Muhammad Nabeel Mustafa
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引用次数: 7

摘要

信息过载是许多电子商务平台用户的潜在挫折之一。当用户在网上商店做出购买决定时,过滤媒体和选择是非常重要的。为了解决这个问题,推荐系统得到了广泛的应用。推荐系统通过过滤和优先级,有效地为用户生成相关信息,帮助用户找到自己选择的产品。推荐系统的目的是为了节省在万维网上搜索的时间和麻烦,而是生成特定的、相关的内容,促进网上交易,使电子商务平台的用户满意。所提出的系统是一个基于服装推荐系统的电子商务平台,根据用户的喜好进行产品推荐。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
E-commerce platform based on Machine Learning Recommendation System
Information overload is one of the potential setbacks to many e-commerce platform users. It is very important to filter the media and the choices that are overwhelming for internet users while making buying decisions using online stores. To solve this problem, recommendation systems are used widely. A recommender system helps users find a product of their own choice by filtering and prioritizing and effectively generating the relevant information to its users. The purpose of a recommender system is to save time and hassle of searching through the World Wide Web, instead it generates specific and relevant content that promotes online transaction and bring satisfaction to the users of e-commerce platforms. The proposed system is an e-commerce platform based on an apparel recommendation system that recommends products on the foundation of the user's preferences.
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