Bespoke Travel Recommendation System on Social Media
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2018 by IJCTT Journal|
|Year of Publication : 2018|
|Authors : M.saranya ,A.prema|
|DOI : 10.14445/22312803/IJCTT-V60P107|
M.saranya ,A.prema "Bespoke Travel Recommendation System on Social Media". International Journal of Computer Trends and Technology (IJCTT) V60(1):46-52 June 2018. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Nowadays big data is an important thing to handle a large volume of data (terabyte of data). Big data isevolving interms of describing any voluminous amount of structures, semi structured and unstructured data that has potential to be mined for information. Big data is ever more valued both research and industrial areas such as health care, finance service, entertainment, inter technology and commercial recommendation. This paper provides a study of online travel information for bespoke travel package recommendation from both travelogues and community –contributes photos and heterogeneous metadata (e.g., tags, geo-location, and date taken) associated with these photos. We first analyze the characteristics of the travel packages and develop a Tourist-Area-Season Topic (TAST) model, which can extract the topics conditioned on both the tourists and essential features (i.e. locations, travel seasons) of the landscapes. Topical package galaxy containing descriptive tags, the dashes of cost, visiting time and visiting season of each area, is extracted to link the expression gap concerning user travel favorite and travel routes, to recommend bespoke POI (point of interest) sequence, first, famous routes are classified according to the parallel between user package and route package.
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Big data, POI, TAST(Tourist-area-season-topic), Travel recommendation.