A CONTEXT-AWARE NEURAL COLLABORATIVE FILTERING FRAMEWORK FOR PERSONALIZED TRAVEL RECOMMENDATION
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The proliferation of digital tourism platforms creates severe choice overload for travelers. Traditional recommender systems—predominantly Matrix Factorization and Content-Based Filtering—fail to integrate dynamic travel context into the neural latent space, resorting to sub-optimal post-filtering. This paper proposes a Context-Aware Neural Collaborative Filtering (CA-NCF) architecture embedding Season, Budget, and Duration as dense vectors alongside User and Item embeddings, processed through a fine-tuned MLP. A hybrid re-ranking function combining neural scores with review popularity and GPS-based proximity boost (Haversine formula) further refines results. Deployed as a Flask web application with OSRM routing, Nominatim geocoding, and Overpass API hotel discovery, the system transitions recommendations into actionable itineraries. Evaluated on 550+ Indian destinations and 2,300+ user ratings, CA-NCF achieves a final MSE of 0.100 versus 0.191 for a baseline NCF, alongside cold-start resilience and sub-100ms CPU inference.
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V, J. (2026). A Context-Aware Neural Collaborative Filtering Framework for Personalized Travel Recommendation. International Journal of Science, Strategic Management and Technology, 02(03). https://doi.org/10.55041/ijsmt.v2i3.114
V, Jayaraj. "A Context-Aware Neural Collaborative Filtering Framework for Personalized Travel Recommendation." International Journal of Science, Strategic Management and Technology, vol. 02, no. 03, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i3.114.
V, Jayaraj. "A Context-Aware Neural Collaborative Filtering Framework for Personalized Travel Recommendation." International Journal of Science, Strategic Management and Technology 02, no. 03 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i3.114.
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