Title :
Tourism e-commerce recommender system based on web data mining
Author :
Xuesong Zhao ; Kaifan Ji
Author_Institution :
Oxbridge Coll., Kunming Univ. of Sci. & Technol., Kunming, China
Abstract :
Recommender system based on web data mining is widely used in e-commerce for it generates more accurate and objective recommendation results and provides personalized service for web users. This paper makes analysis on some major recommendation methods based on web data mining such as Collaborative Filtering and Association Rules mining, and discusses the practical application of these methods in the tourism e-commerce, and then presents a design of web mining based tourism e-commerce recommender system with offline and online modules.
Keywords :
Internet; collaborative filtering; data mining; electronic commerce; recommender systems; travel industry; Web data mining; Web users; association rules mining; collaborative filtering; offline module; online module; personalized service; recommendation methods; tourism e-commerce recommender system; Data mining; Databases; Electronic mail; Recommender systems; recommender system; tourism e-commerce; web data mining;
Conference_Titel :
Computer Science & Education (ICCSE), 2013 8th International Conference on
Conference_Location :
Colombo
Print_ISBN :
978-1-4673-4464-7
DOI :
10.1109/ICCSE.2013.6554161