EventPilot AI: A Review of Smart Event Booking and Recommendation Platform

Author

Pradnya Meshram, Durgesh Bhagat, Riddhi Gaharwar, Binty Pardhi, Harshad Moundekar

Abstract

 When organizing an event, people must look for and evaluate a variety of service providers, including photographers, DJs, musicians, makeup artists, event hosts, and locations. Factors such as budget, location, ratings, style, availability, and event type make this procedure time-consuming. Content-based, collaborative, hybrid, context-aware, and explainable recommendation strategies can enhance personalization and reduce information overload, according to current recommender-system research. In addition to cold-start handling, review-based recommendation, and conversational interfaces, this evaluation looks at these methods and connects them to EventPilot AI. To produce relevant service recommendations, the suggested platform integrates user needs including event type, budget, location, date, and service category. With the help of an AI-powered conversational assistant, it also offers understandable elements like ratings, availability, style resemblance, and budget fit. A practical gap in combining multi-factor matching, availability-aware booking, explainable recommendations, and conversational support into a single event-planning platform is found in the review. With its application-oriented smart event scheduling and recommendation platform, EventPilot AI fills this gap.

Keywords

Explainable AI, Service Provider Matching, Conversational AI, Hybrid Recommendation, Context-Aware Recommendation, Event Booking, and Recommender Systems

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