10.46243/jst.2023.v8.i07.pp163-168 registered
A Hybrid Framework For Travel Advice System Using Big Data And AI
Resolves to https://www.jst.org.in/index.php/pub/article/view/761
Held by Longman Publishers (India) · prefix 10.46243 live · DOI address https://doi.org/10.46243/jst.2023.v8.i07.pp163-168
Registered 29 Sep 2026 via crossref · record version 2 · last change 29 Sep 2026, 11:59 PM · record sha256 d275d2635427e3ce…
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What the DOI identifies
JournalArticle — an article in a journal · Digital · Visual · en
A Hybrid Framework For Travel Advice System Using Big Data And AI (PrincipalTitle)
Published 2023-08-07
Part of Journal of Science & Technology · ISSN 2456-5660 · volume 8 · issue 7 · pages 163–168
Agents
- Nusrath Zeeshan Nusrath Zeeshan (author)
- Longman Publishers (publisher)
Identifiers DOI 10.46243/jst.2023.v8.i07.pp163-168
Abstract
In recent years, with the development of the internet and technology, the tourism industry has seen a significant increase in tourist numbers. The growing demand for personalized travel experiences has led to the development of travel advice systems for tourism. This helps travel agents find suitable travel destinations for clients, especially those unfamiliar with the location. Advisory systems are becoming more common in everyday activities like social networking and online buying. A hybrid framework for a travel advice system is proposed based on big data and artificial intelligence. The main aim of the system is to provide tourists with personalized travel planning based on user preferences and historical data. This allows the user to quickly locate what they are seeking for without wasting time or effort. It combines the strength of a content-based and collaborative filtering approach. To improve user affinity relationships and the quality of recommendations in the travel industry, a common recommendation filtering algorithm based on designations and user preferences has been proposed. Context-aware advice systems combine software computing and data mining to incorporate user profiles, social media history, and POI (points of interest) data. Suggestion system for a list of tourist attractions adapted to the preferences of tourists. Also acts as a travel planner by developing a detailed program that includes a multi-level framework for the travel advice system. Based on the traveler’s experiences, the ratings (reviews) were also collected and analyzed to make better decisions for new travelers that advise tourist travel locations based on their previously rated venues. The algorithm searches the database for travel opportunities and uses text-mining techniques to find places of interest. the application of intelligent e-tourism consultation in tourism, focusing on interfaces, consultation algorithms, characteristics, and techniques of artificial intelligence. The goal aims to develop a hybrid travel advisory system that leverages intelligent e-tourism advice in the travel industry, focusing on interfaces and recommendations based on big data and artificial intelligence techniques
System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1
Each element by the standard's name (Annex B: reference elements, then administrative) and the Handbook's (in grey), read off the record above.
| Element | Value | In the record |
|---|---|---|
| DOI Name DOI name | 10.46243/jst.2023.v8.i07.pp163-168 | doi |
| Referent Type referentType | Creation | referent |
| Referent Sub-Type referentSubType | JournalArticle — an article in a journal | type |
| Referent Name(s) referentName(s) | A Hybrid Framework For Travel Advice System Using Big Data And AI (PrincipalTitle, en) | titles |
| Basic Metadata basicMetadata | author: Nusrath Zeeshan Nusrath Zeeshan publisher: Longman Publishers published: 2023-08-07 part of: Journal of Science & Technology · ISSN 2456-5660 · vol. 8 · no. 7 · pp. 163–168 language: en form: Digital · Visual · Language | agents, dates, container, language, structural_type, modes, characters |
| Referent Identifier(s) alternateIdentifier(s) | none besides the DOI | identifiers, relations (IsSameAs) |
| Registration Authority registrationAuthorityCode | Crossref — issued by Crossref (member 25296); held here as a copy | record.source_agency (our code, ra_doi_name, for names issued here once appointed) |
| Created Date issueDate | 2024-02-16 | record.registered (when the DOI name was first registered) |
| relatedIdentifiers | none needed — the descriptive metadata is in this record | container, relations (only where the descriptive metadata lives at another identifier) |
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History — the ledger
Every change to this DOI, in order, as it was recorded. Entries are only ever added, never changed or removed.
| # | When | What | By | Changes |
|---|---|---|---|---|
| 1 | 29 Sep 2026, 10:00 PM | register registered at Crossref; record read from api.crossref.org | Administrator (admin) | 80 fields set · sha256 9588bccc7a0b… |
| 2 | 29 Sep 2026, 11:59 PM | update record re-read from api.crossref.org | Administrator (admin) | container.titles.0.value: |
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