A Hybrid Approach in Developing a Recommendation System for Personalized Selection of Locations for a Visit

Abstract

Personalized recommendation systems play a crucial role in enhancing user experiences by providing tailored suggestions based on individual preferences and contextual factors. This paper presents a hybrid approach in developing a recommendation system for selecting locations to visit, integrating user-defined filters, contextual data, and collective user feedback. The proposed system leverages a deep neural network to analyze various inputs, including explicit user preferences (e.g., desired atmosphere, type of location, etc.), dynamic contextual factors (e.g., weather conditions, temperature, etc.), and historical user data (e.g., ratings, recommendation trends for similar preferences, etc.). By combining content-based filtering with collaborative filtering techniques, the model aims to improve the accuracy and relevance of recommendations. The system classifies locations as suitable or unsuitable based on the given criteria, providing users with adaptive and context-aware suggestions. The hybrid nature of the approach allows for a more comprehensive understanding of user needs while incorporating real-time environmental conditions. Experimental validation is conducted to assess the effectiveness of the model in generating accurate recommendations. The results highlight the advantages of integrating multiple data sources and deep learning techniques to enhance accuracy and achieve high-quality recommendations. This research contributes to the development of intelligent recommendation systems by proposing a scalable and adaptable framework for personalized location selection.