Computer Engineering and Intelligent Systems www.iiste.org
ISSN 2222-1719 (Paper) ISSN 2222-2863 (Online)
Vol.4, No.5, 2013
32
Research on Personalized Recommender System for Tourism
Information Service
Huang Yu
Yao Dan Luo Jing
Zhang Mu
*
Shenzhen Tourism College of Jinan University
6 QiaoCheng East Road, Overseas Chinese Town,Shenzhen 518053, Guangdong, China
The research is supported by Science and Technology Planning Project of Guangdong province, China (Project
Number: 2012B031400008)
Abstract
Since the development in the 1990s, Recommender system has been widely applied in various fields. The
conflict between the expansion of tourism information and difficulty of tourists obtaining tourism information
allows Tourism Information Recommender System to have a practical significance. Based on the existing online
tourism information service and the mature recommendation algorithms, Personal Recommender System can be
used to solve present problems of the key recommendation algorithms. In the first place, this research presents an
overview of researches on this issue both at home and abroad, and analyzes the applications of main stream
recommendation algorithms. Secondly, a comparative study of domestic and international tourism information
service websites is conducted. Drawbacks in their applications are defined and advantages are adopted in the
settings of Recommender System. Finally, this research provides the framework of Recommender System, which
combines the design and test of algorithms and the existing tourism information recommendation websites. This
system allows customers to broaden experience of tourism information service and make tourism decisions more
accurately and rapidly.
Keywords: Tourism information service, Personalized recommendation, Intelligence recommendation module,
Apriori algorithm
1. Introduction
With the arrival of information age, tourism industry has expedited its pace of informatization. As a result,
tourism enterprises have grown increasingly dependent on information technology. Since tourists are getting
more and more sophisticated and rational, their needs tend to be diversified. Therefore, it is all the more
challenging to provide them with the requested tourism information. In the current domestic tourism market,
individualized travel is replacing traditional travel mode even though package tour is still the mainstream. To
participate in the individualized activities, tourists have to do a lot of inquiring and retrieving from the vast sea
of tourism information in order to get what they want, which hampers the development of personalized tourism
information service.
As a user-centered service mode, personalized tourism information service supplies tourism information and
services based on the user’s requirements, personality and travel habits. This system aims at providing valuable
information for the users’ reference when they make travel decisions. The personalized recommender system is
capable of catering for different needs, thus truly realizing the goal of service on demand. Personalized
recommendation of tourism information furnishes the development trend for tourism information.
2. Literature Review
The early recommender system focuses on content recommendation, and thus can’t do anything with such
information as music, image and video. To solve this problem, Konston
[1]
advances collaborative filtering
recommendation. It produces recommendations according to the similarity level of the users and other
parameters, so the recommendations are of higher value and timeliness.
In the research of travel recommender system, Schafer designs a system which simulates a travel agent who can
assist the user to get recommendation service online. David contrives an agent-based system named Intelligent
Travel Planning (ITP). It collects and processes travel information and recommends it to the user by dint of
intelligent agents with different functions. Making use of tourist-based textual response, Stanley
[2]
devises a
travel recommender system similar to a decision-support system. It presents information which may be of
interest to travel agencies and tourists. Recommender system develops with the application of artificial
intelligence. E-commerce platform such as Amazon is a case in point. Felfernig
[3]
is the first person who
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