An Overview of Different Types of Recommendations Systems - A Survey

Premkumar Duraisamy, S. Yuvaraj, Yuvaraj Natarajan, V. Niranjani
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引用次数: 1

Abstract

In recent years the boom of internet and social media usage everyone spend their invaluable time in social media app and looking for the solution for all kind of their problems. This work analysis deeply on how recommendation system works and its types in different platforms. Most of the modern recommendation system use machine learning algorithms like linear regression, random forest regression and support vector model with collaborative filtering method. Recommendation is nothing but an choice making system. It is vary from person to person based on their interest, culture, locality, education background, interpersonal skills etc., The huge item can be filtered from one by one based on each parameter and finally it will reach the right recommendation item. The research community has worked tremendous way in the field of recommendation system and produced huge variety of result. This survey enlightening the ideas about variety of recommendation system and techniques used by the research community.
不同类型推荐系统的概述-一项调查
近年来,互联网和社交媒体的蓬勃发展,每个人都把宝贵的时间花在社交媒体应用程序上,寻找各种问题的解决方案。本文深入分析了推荐系统在不同平台上的工作原理及其类型。现代推荐系统大多采用线性回归、随机森林回归、支持向量模型等机器学习算法和协同过滤方法。推荐只不过是一个选择系统。每个人的兴趣,文化,地域,教育背景,人际交往能力等都是不同的,庞大的项目可以根据每个参数逐一过滤,最终得到合适的推荐项目。学术界在推荐系统领域做了大量的工作,取得了各种各样的成果。这一调查启发了学术界对各种推荐系统和推荐技术的思考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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