A clustering-based recommendation engine for restaurants
by Aarti Singh; Anu Sharma
International Journal of Advanced Intelligence Paradigms (IJAIP), Vol. 11, No. 3/4, 2018

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.

Online publication date: Mon, 08-Oct-2018

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