Title :
CROWN: A Context-aware RecOmmender for Web News
Author :
Shaoqing Wang ; Benyou Zou ; Cuiping Li ; Kankan Zhao ; Qiang Liu ; Hong Chen
Author_Institution :
Key Lab. of Data Eng. & Knowledge Eng. of MOE, Renmin Univ. of China, Beijing, China
Abstract :
It is popular for most people to read news online since the web sites can provide access to news articles from millions of sources around the world. For these news web sites, the key challenge is to help users find related news articles to read. In this paper, we present a system called CROWN (Context-aware RecOmmender for Web News) to do Chinese news recommendation. By recommendation, the system can retrieve personalized fresh and relevant news articles to mobile users according to their particular context. Differing from existing mobile news applications which employ rather simple strategies for news recommendation, CROWN integrates the contextual information in prediction by modeling the data as a tensor. Such context information usually includes the time, the location, etc. This demo paper presents the implementation of the whole procedure of news recommendation in the system of CROWN. Experimental results on a large corpus of newly-published Chinese web news show its performance is satisfactory.
Keywords :
Web sites; information retrieval; mobile computing; recommender systems; CROWN; Chinese news recommendation; Web sites; context-aware recommender for Web news; contextual information; mobile users; personalized fresh news article retrieval; relevant news retrieval; Context; Context modeling; Data models; Mobile communication; Predictive models; Tensile stress; Tin;
Conference_Titel :
Data Engineering (ICDE), 2015 IEEE 31st International Conference on
Conference_Location :
Seoul
DOI :
10.1109/ICDE.2015.7113391
