A survey paper on Alleviating cold-start problem in recommendation system using machine learning techniques
ANURAG SINGH, Dr. Subhadra Shaw
recommender system, machine learning, collaborative filtering, systematic review
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
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