doi: 10.7763/IJIET.2012.V2.120
User-based Question Recommendation for Question Answering System
Abstract
An approach to automatically recommending question based on user-word model is proposed. We first employ language modeling approach to map the relationship between a user and a question into the relationship between the user and words in the question. A use-word model is then designed to reveal and quantify the affinity relationship between users and words in the corpus. In the recommendation model, a new question is assigned to users based on the evaluation of question-user relationship. The user who has the strongest relationship with the question is recommended to answer the question. We also implement an incremental update model which can dynamically maintain the user-word model. 216,563 questions (spreading into 30 categories) from Yahoo! Answers are collected as dataset and preliminary experiments show our approach achieves question recommendation accuracy by 85.2%, which exceeds baseline methods.
Keywords
- Question recommendation
- question answering
- collaborative filtering
How to Cite
Gang Liu and Tianyong Hao, "User-based Question Recommendation for Question Answering System," International Journal of Information and Education Technology, vol. 2, no. 3, pp. 243-246, 2012. https://doi.org/10.7763/IJIET.2012.V2.120
Copyright & License
Copyright © 2012 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).