Volume : 4, Issue : 4, MAR 2020


Mr. N. Varatharajan, J. Guruprasad, K. Mathumitha


Recommendation system is widely used for deploying the prediction based on the preferences of users to items. In many places, to find a restaurant in rush hour is a challenging activity to do. It is going to be easy, when someone accesses an application and it recommend the best restaurant then he/she can visit to. In this paper, according to the characteristics of restaurant recommender systems, the restaurant recommendation is done based on improved collaborative and content-based filtering method is proposed to be analyzing the end user’s behaviors. The ICCFM implicitly or explicitly considers the influences of individual or similar user preferences and the relationship. Then this experiment is executed on the yelp data set that our application crawls. This recommender system propose a machine learning algorithms to resolve the issues of personalized restaurant selection relying upon yelp data.


Recommender system; machine learning collaborative filtering; content-based filtering; user inputs and behaviors; user feature.

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