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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM53984

Trip Planning- AI-Powered Recommendation Systems for Trip Planning

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM53984

Volume:

9 |

11 |

Nov 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM53984

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

1T

1Sanmay Tile

2GSK

2Dr. G. Sunil Kumar

3Z

3Aditya Zanjurne

4C

4Aniket Chapole

5U

5Krushna Undge

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Abstract

AI-powered recommendation systems for trip planning help travellers choose the best places to visit, stay, and explore based on their preferences. These systems use artificial intelligence and machine learning to analyse information such as user interests, past trips, budget, location, and weather conditions. By processing large amounts of data, the system suggests personalized travel plans, including destinations, hotels, transportation, and activities. It also improves the user experience by providing quick and accurate recommendations, reducing the time and effort needed for planning. Overall, AI-powered trip planning systems make travel easier, smarter, and more efficient by understanding user needs and offering tailored solutions. AI-powered recommendation systems for trip planning are smart tools that help travellers create better travel plans with less effort. These systems use artificial intelligence to understand the user’s interests, budget, time, and travel style.

KEYWORDS

Trip planning recommendation system personalized travel machine learning travel suggestions

Cite This Article

1Sanmay Tile, 2Dr. G. Sunil Kumar, 3Aditya Zanjurne, 4Aniket Chapole, 5Krushna Undge, "Trip Planning- AI-Powered Recommendation Systems for Trip Planning," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 11, Nov 2025. https://doi.org/10.55041/IJSREM53984

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Publication Date

11/14/2025

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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0

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5

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The authors declare no conflict of interest related to this research work.

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