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In the WWW (World Wide Web), dynamic development and spread of data hasresulted a tremendous amount of information available on the Internet, yet useris unable to find relevant information in a short span of time. Consequently, asystem called recommendation system developed to help users find theirinfromation with ease through their browsing activities. In other words,recommender systems are tools for interacting with large amount of informationthat provide personalized view for prioritizing items likely to be of keen forusers. They have developed over the years in artificial intelligence techniquesthat 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 recommendationsystems into new sets of challenges and problem areas that are yet to be trulysolved, primarily a problem with the case of making a recommendation to a newuser that is called cold-state (i.e. cold-start) user problem where the newuser might likely not yield much of information searched. Therfore, the purposeof this paper is to tackle the said cold-start problem with a few effecientmethods and challenges, as well as identify and overview the current state ofrecommendation system as a whole

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