Authors :
Ankit Senger; Aryan Deol
Volume/Issue :
Volume 11 - 2026, Issue 5 - May
Google Scholar :
https://tinyurl.com/auwdpft9
Scribd :
https://tinyurl.com/ytneyz6s
DOI :
https://doi.org/10.38124/ijisrt/26may2151
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
The proliferation of digital platforms and location-aware mobile technologies has fundamentally reshaped how
consumers access and evaluate local personal care services. This paper introduces an intelligent, multi-criteria salon discovery framework that synthesizes location-based services (LBS), natural language processing (NLP), and machine learning to generate personalized, ranked recommendations. The proposed system leverages real-time GPS positioning, collaborative filtering, sentiment-driven review mining, and a composite weighted scoring model to surface the most suitable salons based on proximity, service quality, pricing transparency, and live availability. Experimental evaluation demonstrates that geo-hash pre-filtering reduces the candidate search space by approximately 60%, while the integrated ranking mechanism yields a 35% improvement in user satisfaction over baseline keyword search approaches. The architecture is designed for cloud-native horizontal scalability and incorporates robust mechanisms for fake review mitigation, data privacy, and ethical ranking fairness.
Keywords :
Location-Based Services, Intelligent Recommendation System, User Preference Analysis, GPS, Review Mining, Sentiment Analysis, Multi-Criteria Decision Making.
References :
- A. Zimmermann, A. Lorenz, and R. Oppermann, "An operational definition of context," in Proc. 6th Int. Conf. Modeling and Using Context, 2007, pp. 558–571.
- J. Ye, X. Zhu, and H. Cheng, "What’s your next move: User activity prediction in location-based social networks," in Proc. SIAM Int. Conf. Data Mining, 2013, pp. 171–179.
- G. Fei, A. Mukherjee, B. Liu, M. Hsu, M. Castellanos, and R. Ghosh, "Exploiting burstiness in reviews for review spammer detection," in Proc. 7th AAAI ICWSM, 2013, pp. 175–184.
- R. Jindal and B. Liu, "Review spam detection," in Proc. 16th Int. Conf. World Wide Web, 2007, pp. 1189–1190.
- E. Triantaphyllou, Multi-Criteria Decision Making Methods: A Comparative Study. Dordrecht: Springer, 2000.
- Y. Koren, R. Bell, and C. Volinsky, "Matrix factorization techniques for recommender systems," Computer, vol. 42, no. 8, pp. 30–37, 2009.
- C. C. Aggarwal, Recommender Systems: The Textbook. Cham: Springer, 2016.
- D. Jurafsky and J. H. Martin, Speech and Language Processing, 3rd ed. Upper Saddle River, NJ: Prentice Hall, 2023.
The proliferation of digital platforms and location-aware mobile technologies has fundamentally reshaped how
consumers access and evaluate local personal care services. This paper introduces an intelligent, multi-criteria salon discovery framework that synthesizes location-based services (LBS), natural language processing (NLP), and machine learning to generate personalized, ranked recommendations. The proposed system leverages real-time GPS positioning, collaborative filtering, sentiment-driven review mining, and a composite weighted scoring model to surface the most suitable salons based on proximity, service quality, pricing transparency, and live availability. Experimental evaluation demonstrates that geo-hash pre-filtering reduces the candidate search space by approximately 60%, while the integrated ranking mechanism yields a 35% improvement in user satisfaction over baseline keyword search approaches. The architecture is designed for cloud-native horizontal scalability and incorporates robust mechanisms for fake review mitigation, data privacy, and ethical ranking fairness.
Keywords :
Location-Based Services, Intelligent Recommendation System, User Preference Analysis, GPS, Review Mining, Sentiment Analysis, Multi-Criteria Decision Making.
