UGC Approved Journal no 63975(19)

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Published in:

Volume 5 Issue 6
June-2018
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR1806399


Registration ID:
183534

Page Number

84-92

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Title

PERSONALIZED TRAVEL RECOMMENDATION SYSTEM FOR TOUR PLANNING

Abstract

In this dissertation we presented a personalized travel sequence recommendation system that uses travelogues data, community contributed data which includes images, heterogeneous metadata (e.g. Tags, geo-location, and date taken) associated with those images to recommend users the Point of Interest (POIs) to visit. We had constructed a Topical package space by collecting travelogues, community Contributed data. The system will analyze this synthetic data set to get the maximum Point of Interest between sources to destination of user travel route within specified distance. Unlike most existing travel recommendation approaches, our approach is not only personalized to users travel interest but also able to recommend a travel sequence rather than individual Points of Interest (POIs).Travelogues data is the data collected from travel sites (e.g. www.hellotravel.com) that offer rich depictions about historic points and voyaging background composed by clients. Moreover, group contributed data with metadata (e.g., photos, POIs, date taken, tags and so on.) collected from social media sites record client’s day by day life and travel understanding. But can this information from various travelogue sites and group contributed sites used to recommend user a well-planned travel sequence such that maximum POIs other than individual POIs would be covered the current system would fail here. Hence the proposed system provides personalized travel sequence recommendation to users by analyzing his or her Topical interests and recommends POIs the user should visit. We used Naïve Bayes algorithm to obtained suitable packages that user fits in also to we used Content Based Collaborative Filtering for recommendation of POIs the user should visit. To obtain results such that maximum Point Of Interest would be covered we used Greedy algorithm so that we can get optimized results with maximum POIs.

Key Words

Travel Recommendation, Topical Package Space, Point Of Interests (POIs), Naïve Bayes, Content Based Collaborative Filtering, Greedy Algorithm

Cite This Article

"PERSONALIZED TRAVEL RECOMMENDATION SYSTEM FOR TOUR PLANNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 6, page no.84-92, June-2018, Available :http://www.jetir.org/papers/JETIR1806399.pdf

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2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"PERSONALIZED TRAVEL RECOMMENDATION SYSTEM FOR TOUR PLANNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 6, page no. pp84-92, June-2018, Available at : http://www.jetir.org/papers/JETIR1806399.pdf

Publication Details

Published Paper ID: JETIR1806399
Registration ID: 183534
Published In: Volume 5 | Issue 6 | Year June-2018
DOI (Digital Object Identifier):
Page No: 84-92
Country: Kopargaon, Ahmednagar, Maharastra, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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