MoodMatch: AI-Powered Destination Discovery Based on Traveler Personality & Emotions

Abstract

Most current travel tools focus on basics like cost, timing, space left, or how crowded a place gets missing the inner reasons people choose trips at all. What drives someone often hides beneath surface needs. MoodMatch steps in here. Built around artificial intelligence, it shifts focus toward emotional triggers and personal goals. Instead of listing options based purely on location trends, it weighs mood patterns alongside who you're traveling with. Suggestions reflect deeper preferences, not just outward facts. A weekend escape feels different when shared with friends versus family the system notices these layers. Personal history matters too. Past reactions shape future choices, so past behavior informs new prompts. Matching places to mindset becomes the core logic. Decisions stem from internal cues rather than external rankings. The outcome? Trips that fit not because they are cheap or close, but because they resonate. Starting from well-known theories like Crompton’s 1979 idea of push-pull motives, the approach builds on Pearce’s later work about travel stages. Because it includes Iso-Ahola’s concept of seeking freedom through leisure, the structure gains depth. Rather than relying on a single viewpoint, it draws from Kim, Ritchie, and McCormick’s 2012 method for measuring tourism memories. Through such integration, traveler goals are grouped under eight headings. These include inner growth, time with others, enjoyment, unwinding, getting away, discovery, looking inward, and gaining knowledge. Delivered via both a web app, the system offers one route built around an inference-driven survey, another supporting smooth chat-style engagement. Depending on needs, people find their way using real-world criteria - like whether they are family or friends, how many travelers there are, what they aim to spend, how long they plan to stay, even when they want to go. Among those surveyed, relaxation stands out as a central reason people travel. A study involving 207 participants used fixed-response questions alongside open conversations to test core ideas. Satisfaction rates came in above average - three out of four said they were happy with their trips. What disrupted some journeys was lack of preparation or difficulties within traveling groups. These issues align directly with what the tool aims to resolve. Seasonal timing, financial limits, and how easy it is to reach destinations shape most choices. Instead of abstract theories, real behavior showed patterns tied to connection, discovery, and unwinding. When plans fell short, mismatches in expectations played a large role. Interviews added depth, showing emotional context behind numbers. Most decisions filter through logistics before anything else. Experiences missing the mark often stemmed from coordination breakdowns. High engagement did not guarantee fulfillment. Through combined data forms, motivation became clearer. Planning flaws surfaced repeatedly across responses. Trips meant for rest sometimes failed due to crowded schedules. Social goals mattered just as much as scenic ones. Every tenth person mentioned stress despite being on vacation. Practical barriers outweigh internal desires when booking begins. Not every explorer feels fulfilled upon return. The design of MoodMatch responds to these specific pain points. Rather than treating trips as simple exchanges, MoodMatch fosters personal insight, deeper connections with local cultures, while supporting development long after journeys end.