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http://dx.doi.org/10.3837/tiis.2017.06.009

OLAP4R: A Top-K Recommendation System for OLAP Sessions  

Yuan, Youwei (School of Computer Science and Technology, Hangzhou Dianzi University)
Chen, Weixin (School of Computer Science and Technology, Hangzhou Dianzi University)
Han, Guangjie (Department of Information and Communication Systems, Hohai University)
Jia, Gangyong (School of Computer Science and Technology, Hangzhou Dianzi University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.11, no.6, 2017 , pp. 2963-2978 More about this Journal
Abstract
The Top-K query is currently played a key role in a wide range of road network, decision making and quantitative financial research. In this paper, a Top-K recommendation algorithm is proposed to solve the cold-start problem and a tag generating method is put forward to enhance the semantic understanding of the OLAP session. In addition, a recommendation system for OLAP sessions called "OLAP4R" is designed using collaborative filtering technique aiming at guiding the user to find the ultimate goals by interactive queries. OLAP4R utilizes a mixed system architecture consisting of multiple functional modules, which have a high extension capability to support additional functions. This system structure allows the user to configure multi-dimensional hierarchies and desirable measures to analyze the specific requirement and gives recommendations with forthright responses. Experimental results show that our method has raised 20% recall of the recommendations comparing the traditional collaborative filtering and a visualization tag of the recommended sessions will be provided with modified changes for the user to understand.
Keywords
On-Line Analytical Processing (OLAP); recommend system; date mining; big data; label generate;
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