A. Sharmila Banu, M. Abirami, Panimalar Institute of Technology, Chennai, Tamilnadu, India.
DOI : 01.0401/ijaict.2015.12.07
International Journal of Advanced Information and Communication Technology
Received On : June 10, 2018
Revised On : July 15, 2018
Accepted On : August 10, 2018
Published On : September 05, 2018
Volume 05, Issue 09
Pages : 945-949
Abstract
As the worlds of business, entertainment, travel and Internet technology become more linked, new types of business data become available for creative use and formal analysis. This project provides a study of online travel information for personalized travel package suggestion to the best course of travel. A target along this line is to address the unique characteristics of travel data, which differentiates travel packages from traditional items for recommendation. The characteristics of the travel packages, tourist feedback, season are analyzed and used for proposing on personalized travel package recommendation.A tourist-area-season topic (TAST) model is developed to represent travel packages and tourists by different topic distributions, where the topic extraction is conditioned on both the tourists and the intrinsic features (i.e., locations, travel seasons) of the landscapes. This also provides the tourist information and tourist feedbacks to evaluate apackage for recommendation. The experimental results show that the approach is thus much more effective than traditional recommendation methods for travel package recommendation.
Keywords
Travel Package, Recommender Systems, Cocktail, Topic Modeling, Collaborative Filtering
Cite this article
A. Sharmila Banu, M. Abirami, “A versatile Approach for Travel Package Recommendation using Cocktail Algorithm” INTERNATIONAL JOURNAL OF ADVANCED INFORMATION AND COMMUNICATION TECHNOLOGY, pp.945-949, September 05, 2018.
Copyright
© 2018 A. Sharmila Banu, M. Abirami. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.