MATEC Web of Conferences
Volume 44, 20162016 International Conference on Electronic, Information and Computer Engineering
|Number of page(s)||5|
|Section||Computer, Algorithm, Control and Application Engineering|
|Published online||08 March 2016|
An Agent Framework of Tourism Recommender System
Yunnan University, Tourism and Culture College, 674100 Lijiang, China
2 Yunnan Normal University, School of Information Science, 650500 Kunming, China
a Corresponding author: firstname.lastname@example.org
This paper proposes the development of an Agent framework for tourism recommender system. The recommender system can be featured as an online web application which is capable of generating a personalized list of preference attractions for tourists. Traditional technologies of classical recommender system application domains, such as collaborative filtering, content-based filtering and content-based filtering are effectively adopted in the framework. In the framework they are constructed as Agents that can generate recommendations respectively. Recommender Agent can generate recommender information by integrating the recommendations of Content-based Agent, collaborative filtering-based Agent and constraint-based Agent. In order to make the performance more effective, linear combination method of data fusion is applied. User interface is provided by the tourist Agent in form of webpages and mobile app.
© Owned by the authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.