• Y. PALLAVI
• Dr. K. Madhavi

Reviews on various services and products made available over Internet have been playing important role in helping people to make well informed decisions. Different services and their ratings are provided online so as to help prospective customers to understand the companies that offer better services. Therefore, online reviews became goldmine for researchers and academicians to build new algorithms and frameworks that can produce useful information to general public. Many researchers contributed towards mining online reviews to gain business intelligence. In this paper a framework has been projected that generates personalized Point of Interest (POI) based service recommendations using unsupervised learning methods. The proposed methodology has an underlying algorithm named POI Based Service Recommendations (PBSR) to achieve this. Datasets are collected from YELP portal. We built a prototype application to demonstrate proof of the concept. The empirical results revealed that the proposed system is capable of generating personalized service recommendations that are useful to prospective customers to make well informed decisions.

Service rating prediction, online reviews, recommendations, user point of interest.

"POI-BASED SERVICE RATING PREDICTION FOR PERSONALIZED RECOMMENDATIONS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 7, page no.82-87, July-2018, Available :http://www.jetir.org/papers/JETIR1807480.pdf

"POI-BASED SERVICE RATING PREDICTION FOR PERSONALIZED RECOMMENDATIONS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 7, page no. pp82-87, July-2018, Available at : http://www.jetir.org/papers/JETIR1807480.pdf