OLAP4R: A Top-K Recommendation System for OLAP Sessions

• Youwei Yuan,
• Weixin Chen,
• Guangjie Han,
• Gangyong Jia,

Vol. 11, No. 6, June 29, 2017
10.3837/tiis.2017.06.009, Download Paper (Free):

• On-Line Analytical Processing (OLAP)
• recommend system
• date mining
• Big data
• label generate

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.

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