The use of Recommender Systems in web technology and an in-depth analysis of Cold State problem

TL;DR

This paper reviews the role of recommender systems in web technology, focusing on the cold-start problem, and discusses recent methods and challenges in addressing this issue within personalized online services.

Contribution

It provides an in-depth analysis of the cold-start problem in recommender systems and surveys current methods and challenges for improving recommendations for new users.

Findings

Identifies key challenges in cold-start recommendation scenarios.

Summarizes recent approaches to mitigate cold-start issues.

Highlights ongoing research directions in recommender system development.

Abstract

In the WWW (World Wide Web), dynamic development and spread of data has resulted a tremendous amount of information available on the Internet, yet user is unable to find relevant information in a short span of time. Consequently, a system called recommendation system developed to help users find their infromation with ease through their browsing activities. In other words, recommender systems are tools for interacting with large amount of information that provide personalized view for prioritizing items likely to be of keen for users. They have developed over the years in artificial intelligence techniques that include machine learning and data mining amongst many to mention. Furthermore, the recommendation systems have personalized on an e-commerce, on-line applications such as Amazon.com, Netflix, and Booking.com. As a result, this has inspired many researchers to extend the reach of recommendation systems into new sets of challenges and problem areas that are yet to be truly solved, primarily a problem with the case of making a recommendation to a new user that is called cold-state (i.e. cold-start) user problem where the new user might likely not yield much of information searched. Therfore, the purpose of this paper is to tackle the said cold-start problem with a few effecient methods and challenges, as well as identify and overview the current state of recommendation system as a whole