In today's digital world, we're surrounded by many events happening around us from music concerts and food festivals to professional workshops and community meetups. While platforms like Eventbrite and Meetup make event information easily accessible, users often feel overwhelmed by the huge volume of listings that rarely match their personal interests or consider where they actually are. This research tackles this problem by developing a smart event discovery system that learns what users like and where they are located to suggest events they'd genuinely want to attend. The system works by combining three different recommendation approaches. It looks at what similar users have enjoyed then it analyzes event descriptions and categories to find events matching a user's stated preferences. and most importantly, it considers how far each event is from the user's location, because even the perfect event isn't useful if it's too far away. These three factors are balanced, with collaborative filtering contributing 40%, content matching 30%, and location convenience 30%. To test the system, we used real event data from Eventbrite and Meetup, including events across 25 different categories, multiple users, and user interactions like event views, saves, and attendance our system correctly identified relevant events most of the time. When recommending ten events to a user, nearly nine out of ten matched their interests, and the system captured more than seven out of every ten events users actually wanted. These numbers significantly outperformed traditional recommendation methods. We also conducted an ablation study to understand each component's contribution. Removing the location factor dropped performance noticeably, confirming that where an event happens matters almost as much as what it's about. A user study with 50 participants reinforced these findings people rated our system 4.3 out of 5, significantly higher than the 3.8 rating for existing approaches. Participants particularly appreciated getting suggestions for events within reasonable traveling distance. This research shows that combining what users like with where they can creates genuinely helpful event recommendations. The approach can benefit various applications from helping tourists discover local happenings to connecting community members with neighborhood activities and supporting smart city initiatives that bring people together.
Event discovery, Recommender system, User preferences, Location-based services, Collaborative filtering, Hybrid recommendation, Context-aware computing, Location-based social networks, Geospatial analysis, Personalization
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