A survey paper on Alleviating cold-start problem in recommendation system using machine learning techniques
Author(s):
ANURAG SINGH, Dr. Subhadra Shaw
Keywords:
recommender system, machine learning, collaborative filtering, systematic review
Abstract
The aim of recommendation systems is to provide users with items that they may be interested in. However, one of the most serious issues for systems to recommend is a problem known as cold start, which happens when new users or items are introduced to the system with no previous knowledge of them. There are many proposals in the literature that aim to deal with this issue. In some cases the user is required to provide some explicit information about them, which demands some effort on their part. In this paper we will introduce how communication information will be used to create a behavioral profile to differentiate users and based on this section will create predictions using machine learning methods. This paper conducts a systematic analysis of the literature to assess the use of machine learning techniques in recommendation systems and to identify areas for further study. The overall survey of this paper will address the research gap and opportunities with the Recommendations system(RS).
Article Details
Unique Paper ID: 153296

Publication Volume & Issue: Volume 8, Issue 6

Page(s): 384 - 392
Article Preview & Download


Share This Article

Conference Alert

NCSST-2021

AICTE Sponsored National Conference on Smart Systems and Technologies

Last Date: 25th November 2021

SWEC- Management

LATEST INNOVATION’S AND FUTURE TRENDS IN MANAGEMENT

Last Date: 7th November 2021

Go To Issue



Call For Paper

Volume 8 Issue 4

Last Date 25 September 2021

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews

Contact Details

Telephone:6351679790
Email: editor@ijirt.org
Website: ijirt.org

Policies