Title: A clustering-based recommendation engine for restaurants

Authors: Aarti Singh; Anu Sharma

Addresses: MM Institute of Computer Technology and Business Management, MMU, Ambala, Haryana, India ' MM Institute of Computer Technology and Business Management, MMU, Ambala, Haryana, India

Abstract: With the wide spread of tourism industry, restaurant recommendation systems have become an important application area for any recommendation systems (RS). Designing an efficient and scalable solution for restaurant recommendation is still an open area of research. Many researchers have contributed to the idea of generating recommendation systems for restaurants. But none of these approaches used clustering of user profile database to reduce the search space before applying recommendation techniques (RT). The aim of this research is to provide a more scalable solution for recommending restaurants. This work applies existing RT on reduced rating data obtained by clustering of user profiles. Results suggested that there is considerable decrease in the processing time while maintaining the accuracy of the recommendation.

Keywords: clustering; k-means; recommendation techniques; user profiling; restaurant recommendation.

DOI: 10.1504/IJAIP.2018.095467

International Journal of Advanced Intelligence Paradigms, 2018 Vol.11 No.3/4, pp.272 - 283

Received: 18 Dec 2015
Accepted: 27 Jan 2016
Published online: 08 Oct 2018 *

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