A Cuisine Based Recommender System Using k-NN and Map Reduce Approach
Sadhana Kodali1, Madhavi Dabbiru2, B Thirumala Rao3
1Sadhana Kodali, Ph.D Scholar, Department of CSE, Koneru Lakshmaiah Education Foundation,Vaddeswaram, Guntur Dt, (Andhra Pradesh), India.
2Madhavi Dabbiru, Professor&Head , Department of Computer Science Engineering, Dr.L.Bullayya College of Engineering for Women,Visakhapatnam (Andhra Pradesh), India.
3Thirumala Rao B, Professor, Department of CSE, Koneru Lakshmaiah Education Foundation,Vaddeswaram, Guntur Dt, (Andhra Pradesh), India.
Manuscript received on 01 May 2019 | Revised Manuscript received on 15 May 2019 | Manuscript published on 30 May 2019 | PP: 32-36 | Volume-8 Issue-7, May 2019 | Retrieval Number: F3373048619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the present days, life can be made smarter, including the food we eat by taking an option 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 restaurants recommended. The recommendations are retrieved based on the preference of the user cuisines which is an important category which inherently defines the other features, and these features are considered to provide a good service which is the novelty of this paper. Providing recommendations based on user cuisines is the complexity of the problem. The well-known k-Nearest Neighbor algorithm is implemented with the MapReduce paradigm which can quickly process huge amounts of data. Its performance is tested on benchmarked data set and the results are found to be successful.
Keyword: Restaurant Recommender System, Nearest Neighbor approach, MapReduce, Cuisine based search.
Scope of the Article: Middleware for Service Based Systems.