MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202641086319 A) filed by Vallurupalli Nageswara Rao Vignana Jyothi Institute Of Engineering Technology on July 14, 2026, for System And Method For Intelligent Travel Recommendation And Itinerary Planning Using Hybrid Semantic Recommendation Models.
Inventors include Dr. A. Brahmananda Reddy; B. Deekshita Chowdary; G. Bala Anthony Shreya; P. Vineeth Reddy; and M. Sai Gowtham Kumar.
The application for the patent was published on July 17, 2026, under issue no. 29/2026.
Abstract: ABSTRACT [0019] With the increasing demand for personalized and adaptive travel planning, traditional recommendation systems fall short due to their static nature, limited contextual awareness, and challenges like data sparsity and the cold-start problem. Building upon insights gathered from an extensive literature review, this paper presents the practical implementation of a Data-Driven Hybrid Recommendation System for Trip and Itinerary Planning using a Semantic Approach. The new system incorporates multiple recommendation paradigms collaborative filtering, content-based filtering, and sentiment analysis - that leverage semantic tagging, and contextual data, to provide dynamic, context-based travel suggestions. The system's architecture is predicated on a hybrid engine that utilizes user-generated content (the original user contributed reviews, past activity and preferences), location-based data (e.g., GPS, and map APIs), and a set of contextual triggers (for example weather, time, traffic, or a location-based cultural event). [0020] Natural Language Processing (NLP) methods, in conjunction with clustering and filtering algorithms, are utilized to generate destination and activity suggestions that consider both individual and collective destination and activity behaviors by extracting semantic meaning from destination descriptions and user reviews. The model has a built-in real-time feedback loop, so that user inputs in the planning phases sensibly modified additional recommendation suggestions which allows for adaptive personalization. Implementation results indicate improvements in the quality of the suggestions and contextually-appropriate suggestions, and therefore illustrate the benefits of integrating semantic technologies with hybrid recommendation models. This research offers a scalable and intelligent recommendation framework to satisfy the changing expectations of the modern traveler with an intelligent, user-centric, and context-aware itinerary planning.
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