An Approach to Design and Development Recommender System
DOI:
https://doi.org/10.26438/ijcse/v6i3.431433Keywords:
Recommender System, content-based, collaborative filtering, knowledge based filtering, IoTAbstract
Each day we are surrounded by any number of decisions to make. Which book should I read next? Which movie to watch? Which book to read? Which blog to follow? Or which item to buy? Finding the appropriate choice is like finding a needle in a haystack. Increasingly, we use the web and online resources to help us make a decision. As our decision making is transported and conducted in the online sphere, the use of recommendation systems has become essential in daily life. Recommendation systems have been studied and developed for more than two and a half decades. Within this period, a variety of algorithms has been developed for various application domains. The major breakthrough in development of recommender system was in 2006 when Netflix announced the $1 million to whoever improved the accuracy of his existing system called Cinematch by 10%in a machine learning and data mining competition for movie rating prediction.
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