NDLI: A decision tree based recommendation system for tourists

Content Provider	IEEE Xplore Digital Library
Author	Thiengburanathum, P. Shuang Cang Hongnian Yu
Copyright Year	2015
Description	Author affiliation: Fac. of Sci. & Technol., Bournemouth Univ., Bournemouth, UK (Thiengburanathum, P.; Shuang Cang; Hongnian Yu)
Abstract	Choosing a tourist destination from the information that is available on the Internet and through other sources is one of the most complex tasks for tourists when planning travel, both before and during travel. Previous Travel Recommendation Systems (TRSs) have attempted to solve this problem. However, some of the technical aspects such as system accuracy and the practical aspects such as usability and satisfaction have been neglected. To address this issue, it requires a full understanding of the tourists' decision-making and novel models for their information search process. This paper proposes a novel human-centric TRS that recommends destinations to tourists in an unfamiliar city. It considers both technical and practical aspects using a real world data set we collected. The system is developed using a two-steps feature selection method to reduce number of inputs to the system and recommendations are provided by decision tree C4.5. The experimental results show that the proposed TRS can provide personalized recommendation on tourist destinations that satisfy the tourists.
Starting Page	1
Ending Page	7
File Size	566830
Page Count	7
File Format	PDF
e-ISBN	9780992680114
DOI	10.1109/IConAC.2015.7313958
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-11
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Chinese Automation and Computing Society in the UK - CACS
Subject Keyword	Filtering Decision Tree Tourist Destination Decision making Data acquisition Recommendation System Feature Selection Accuracy Distributed databases Classification Internet Decision trees Filtering methods Mutual information
Content Type	Text
Resource Type	Article

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