Research Article
An End-to-end Tag-based Recommendation System for Verbal Reasoning Questions
@INPROCEEDINGS{10.1145/3173519.3173530, author={Zhixiong Yue and Yinghao Jiang and Dong Pan and Zongwei Luo}, title={An End-to-end Tag-based Recommendation System for Verbal Reasoning Questions}, proceedings={10th EAI International Conference on Simulation Tools and Techniques}, publisher={ACM}, proceedings_a={SIMUTOOLS}, year={2018}, month={8}, keywords={recommendation system personalization service user tagging text tagging cold-start problem nature language processing}, doi={10.1145/3173519.3173530} }- Zhixiong Yue
Yinghao Jiang
Dong Pan
Zongwei Luo
Year: 2018
An End-to-end Tag-based Recommendation System for Verbal Reasoning Questions
SIMUTOOLS
ACM
DOI: 10.1145/3173519.3173530
Abstract
Developing a verbal reasoning question1 recommendation system is an ideal way to help the GRE® test takers improve their verbal reasoning abilities by practicing questions more efficiently. As there are a great number of verbal reasoning practice questions and limited practice time for test takers, it is impossible to practice all kinds of questions at the same time. Personalized referral systems should be built based on the characteristics of specific respondents, and forming professional recommendation systems for different questions. Based on the examinee’s current practicing accuracy and fallible difficulties, we propose an End-to-end Tag-based Recommendation System (ETRS) for task takers to optimize practice effect. Code of this paper can be found on https://github.com/Oliver-Q/ETRS-for-Verbal-Reasoning-Questions.