OLAP4R: A Top-K Recommendation System for OLAP Sessions
Youwei Yuan, Weixin Chen, Guangjie Han, Gangyong Jia · KSII Transactions on Internet and Information Systems · 2017
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.