AI-Based Travel Recommendation Systems: Personalizing Tourist Experiences through Preference Modeling and Real-Time Behavioral Analytics in Nigeria

Publication Details

Publication Date

10/10/2026

Volume / Issue

Vol 1, Issue 5 (2026)

Article No.

012

Journal

British Journal of Contemporary Research

DOI

https://doi.org/10.67693/BJCR-VK7LXI74

Received

18 Jul 2026

Views

20

Downloads

13

Abstract

The rapid digitalization of tourism has increased the demand for intelligent recommendation systems capable of delivering personalized travel experiences that adapt to users' evolving preferences and contextual circumstances. Conventional tourism recommendation platforms predominantly rely on static filtering mechanisms and explicit user ratings, often producing generic recommendations that fail to reflect dynamic travel behaviors and real-time contextual information. This study presents the design, development, and evaluation of an Artificial Intelligence (AI)-based travel recommendation system that integrates explicit user preferences, implicit behavioral analytics, contextual awareness, sentiment analysis, and machine learning techniques to enhance personalized tourism experiences in Nigeria. Adopting a mixed-methods research design within a Design Science Research (DSR) framework, the study combines quantitative surveys, behavioral data acquisition, system development, and experimental evaluation. Tourist demographic information, browsing history, clickstream behavior, geo-location data, travel reviews, and contextual variables were collected and preprocessed through data cleaning, normalization, feature engineering, and sentiment extraction before model training. The proposed hybrid recommendation engine combines collaborative filtering, content-based recommendation, Long Short-Term Memory (LSTM) networks, and contextual recommendation algorithms to generate adaptive travel suggestions based on continuously evolving user interactions. Experimental evaluation demonstrates that the hybrid model outperforms conventional recommendation approaches, achieving superior recommendation accuracy, ranking quality, user engagement, and satisfaction. The incorporation of explainable AI mechanisms and ethical data governance further enhances transparency, user trust, and responsible AI adoption. The proposed framework contributes to intelligent tourism research by providing a scalable, context-aware recommendation architecture suitable for developing economies while supporting smart tourism initiatives, destination visibility, and sustainable digital transformation within Nigeria.

License

This article is published under the Creative Commons Attribution 4.0 International License . Free to read, share, and adapt with attribution.