Recommendation Systems for Community Commerce

Abstract

Recommendation systems have become very important for any application nowadays. In this paper, we have researched many such state-of-the-art techniques in the field of advertising and recommendations through various implementations. Applications of such multiple techniques are also discussed along with their limitations. Implementations in the domains of artificial intelligence, the semantic web, IoT, etc. have been studied thoroughly. Important issues like finding the right products for customers and marketing a product to the exact type of customers have been identified and solved with the help of a proper recommendation system. Various models along with their merits and demerits have been studied. This paper also thoroughly explains about the challenges faced by the various models and their respective algorithms which provides an efficient recommendation system to the users. Apart from this a literature survey has also been done taking reference from multiple papers and summarising their outcomes and summing up into this one survey paper. The Internet across the world is filled with millions of data wherein the number of choices is enormous which produces a need to filter, hierarchize, personalize and structure the data so that information overload problem can be deadened.