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In the present days, life can be made smarter, including the food we eat by taking a decision from the restaurant recommender systems. In this paper the authors proposed a restaurant recommender system based on the search of user cuisine.The top-k restaurants are identified along with the ratings of the bistros recommended. The recommendations are recuperated considering the tendency of the client cuisines which is an important category which inherently defines the other features and these features are considered to give a good service which is the novelty of this paper.Providing recommendations considering client cooking styles is the multifaceted nature of the problem. The well known k-Nearest Neighbor algorithm is implemented with the Map Reduce paradigm which can quickly process tremendous proportions of data. Its show is tested on bench marked dataset and the results are found to be successful. Index Terms: Restaurant Recommender System, Nearest Neighbor approach, MapReduce, Cuisine based search. References 1. C.C.Aggarwal,RecommenderSystems:TheTextbook,SpringerInternationalPublishingSwitzerland2016. 2. Zhi-Dan Zhao, Ming-Sheng Shang, ”User-based Collaborative-FilteringRecommendation Algorithms on Hadoop” Third International ConferenceonKnowledgeDiscoveryandDataMining2010. 3. Anjali Gautam and PunamBedi, ”Developing content-based recommendersystem using Hadoop Map Reduce”, Journal of Intelligent & FuzzySystems32 (2017)2997–3008,2017. 4. BidyutKr.Patra,RaimoLaunonen,VilleOllikainen,SukumarNandi,”AnewsimilaritymeasureusingBhattacharyya coefficientforcollaborativefilteringinsparsedata”,KNOSYS3090,12March2015. 5. ChenyangLiKejingHe“CBMR:AnoptimizedMapReduceforitem-basedcollaborativefilteringrecommendationalgorithmwithempirical analysis”, Concurrency and computation Practice and experienceVolume29,Issue1025 May2017. 6. Moon-HeePark,Han-SaemPark,andSung-BaeCho“RestaurantRecommendation for Group of People in Mobile Environments UsingProbabilistic Multi-criteria Decision Making”, APCHI 2008, LNCS 5068,pp.114–122,2008.