[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-377481-105":53,"doc-detail-377481-en":127},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":120,"head_meta":122,"extra_data":124,"updated_unix":126},105,"en","personalized-travel-itinerary-generation-using-large-language-models-and-generative-ai","Personalized Travel Itinerary Generation Using Large Language Models and Generative AI","","Rapid evolution of digital tourism platforms creates demand for automated systems that convert scattered travel information into cohesive, highly personalized itineraries. Traditional tools often deliver isolated recommendations, while travelers expect detailed daywise plans that match real behavior, practicality, and comfort. The proposed system structures user requirements into logical sequences resembling experienced human coordinators, synthesizing costs, preferences, and destination characteristics simultaneously to deliver intuitive, realistic trip schedules.",{"@graph":63,"@context":119},[64,80,102],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":11,"@type":70,"position":76},"https://docshare.wps.com/template/presentations/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/personalized-travel-itinerary-generation-using-large-language-models-and-generative-ai/377481/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/personalized-travel-itinerary-generation-using-large-language-models-and-generative-ai/377481.png","ImageObject",442,249,{"name":88,"@type":89},"lilac","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-28","2026-09-24",true,{"@type":98,"interactionType":99,"userInteractionCount":101},"InteractionCounter",{"@type":100},"ViewAction",10,{"@type":103,"mainEntity":104},"FAQPage",[105,111,115],{"name":106,"@type":107,"acceptedAnswer":108},"Why is automated itinerary generation needed for modern travelers?","Question",{"text":109,"@type":110},"Travelers increasingly face fragmented information across many platforms, making it mentally taxing to assemble coherent multi-day plans. 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While traditional systems offer isolated recommendations, users increasingly expect detailed daywise plans that reflect real travel behavior, practicality, and comfort. The complexity of modern travel decisionmaking—shaped by cost fluctuations, diverse preferences, and varying destination characteristics—demands solutions capable of synthesizing multiple factors simultaneously. The system discussed in this research addresses these expectations by structuring user requirements into logical sequences that mirror the planning style of experienced human travel coordinators. This enhanced capability results in itineraries that feel both intuitive and realistic, bridging the gap between raw data and meaningful travel experiences.Unlike conventional recommendation platforms that provide isolated suggestions, the system emphasizes the creation of complete, day-wise itineraries that reflect realistic travel behavior and geographical flow. It aims to reduce planning fatigue and improve decision-making efficiency by ensuring that suggested activities align with human comfort levels, time availability, and practical feasibility. The methodology prioritizes organization, clarity, and sequencing, ensuring that travelers receive itineraries that not only highlight important attractions but also provide a balanced distribution of activities across the entire trip.  \nKeywords— Artificial Intelligence, Large Language Models, Travel Recommendation Systems, Personalization, FastAPI, Generative AI.  \nI. INTRODUCTION  \nTourism has undergone a significant transformation, with travelers increasingly seeking autonomy and personalization in planning. Unlike earlier decades where travel agencies dominated trip planning, modern travelers rely heavily on digital resources such as travel blogs, review sites, maps, and social media recommendations. While these platforms provide a rich set of choices, they lack the ability to synthesize information into coherent, easy-to-follow itineraries. As a result, individuals spend a substantial amount of time researching, comparing, and organizing activities to match their personal interests, available time, and travel style.  \nVishal Tiwari Department of Information  \nTechnology MMMUT Gorakhpur, India [2022071077@mmmut.ac.in](2022071077@mmmut.ac.in)  \nPrachi Verma Assistant Professor Department of Information Technology MMMUT Gorakhpur, India [prachi.verma1499@gmail.com](prachi.verma1499@gmail.com)  \nThe complexity of travel planning arises not from a lack of information but from an excess of fragmented data scattered across multiple sources. A traveler may find hotel suggestions on one platform, must-visit attractions on another, and local transportation options on yet another. Connecting these elements into a logical sequence becomes mentally taxing, especially for multi-day or multi-city trips. Moreover, travelers value balance—ensuring the plan includes not only sightseeing but also adequate rest, meal breaks, and travel buffer time.  \nThe difficulty lies not in accessing information, but in organizing it in a way that aligns with individual travel styles. A planned trip must respect personal preferences, daily energy levels, destination characteristics, and logistics such as t","cbCaidplUgKG4uhY","https://ap.wps.com/l/cbCaidplUgKG4uhY","pdf",278839,6,"English","# Abstract\n# Keywords\n# I. INTRODUCTION","[{\"question\":\"Why is automated itinerary generation needed for modern travelers?\",\"answer\":\"Travelers increasingly face fragmented information across many platforms, making it mentally taxing to assemble coherent multi-day plans. Automated systems address this by organizing inputs into structured day-by-day schedules.\"},{\"question\":\"What limitations do conventional recommendation platforms have?\",\"answer\":\"They usually provide isolated suggestions rather than complete, day-wise itineraries. This makes it harder to ensure geographic flow, balanced activity distribution, and practical feasibility.\"},{\"question\":\"How does the proposed approach improve itinerary planning?\",\"answer\":\"It structures user requirements into logical sequences that mirror experienced human planners. The method aligns activities with comfort levels, time availability, and feasibility to reduce planning fatigue and improve decision-making efficiency.\"}]","Personalized Travel Itinerary Generation Using Large Language Models and Generative AI | PDF",1790225523]