Deep Learning for a Reliable Recommendation System

Abstract

Recommendation systems are increasingly essential for helping users navigate large volumes of information by providing personalized suggestions. To address common challenges such as data sparsity, cold-start problems, and a lack of contextual awareness, we propose a novel hybrid model specifically designed for travel recommendations. Our approach integrates three main components: a neural collaborative filtering model to learn user-item interaction patterns, a sequential recommendation module based on user behavior, and a semantic similarity model to capture the meaning of user preferences and item descriptions. These models are combined using a weighted hybridization technique and are further enriched with contextual information related to user profiles and travelrelated features. Experimental evaluation demonstrates that our hybrid system significantly improves recommendation accuracy, relevance, and diversity compared to traditional approaches. This highlights the potential of combining neural architectures and contextual modeling in building more adaptive and effective recommendation systems.