[Objective] This paper tries to modify the existing recommendation model for online medical experts, aiming to more effectively address health-related inquiries. [Methods] First, we identified the latent topics of online health questions with the help of Labeled-LDA model. Then, we defined the doctors’ specialties and better match them with questions. Finally, we evaluated the new model with data from http://www.39.net. [Results] The precision, recall and response adoption rates of the proposed method were 40.4%, 44.0% and 22.9%, which were much higher than those of the existing ones. [Limitations] Our method did not include factors like doctors’ responding time and their resumes. This method could not identify expertise of newly joined doctors who answered few questions. [Conclusions] The proposed model could effectively recommend physicians for patients asking questions online.
潘有能,倪秀丽. 基于Labeled-LDA模型的在线医疗专家推荐研究*[J]. 数据分析与知识发现, 2020, 4(4): 34-43.
Pan Youneng,Ni Xiuli. Recommending Online Medical Experts with Labeled-LDA Model. Data Analysis and Knowledge Discovery, 2020, 4(4): 34-43.
( Xie Wenzhao, Gong Xueqin, Luo Aijing . Current Situation and Challenges of Internet Medicine in Our Country[J]. Chinese Journal of Medical Library and Information Science, 2016,25(9):6-9.)
( Zhu Li, Yue Aizhen . Routing Health-Oriented Questions to Appropriate Doctors[J]. Journal of Xi’an Jiaotong University, 2014,48(12):57-62.)
[4]
Balog K, Azzopardi L, de Rijke M. A Language Modeling Framework for Expert Finding[J]. Information Processing & Management, 2009,45(1):1-19.
doi: 10.1016/j.ipm.2008.06.003
[5]
厉超 . 论坛专家发现系统的研究与实现[D]. 广州: 华南理工大学, 2009.
[5]
( Li Chao . Research and Implementation of BBS Expert Discovery System[D]. Guangzhou: South China University of Technology, 2009.)
[6]
Cao Y, Liu J, Bao S, et al. Research on Expert Search at Enterprise Track of TREC 2005[C]// Proceedings of the 14th Text Retrieval Conference, Gaithersburg, Maryland, USA. 2005.
[7]
Kleinberg J M . Authoritative Sources in a Hyperlinked Environment[J]. Journal of the ACM, 1999,46(5):604-632.
doi: 10.1145/324133.324140
[8]
Page L . The PageRank Citation Ranking: Bringing Order to the Web[R]. Stanford InfoLab, 1999.
[9]
Dom B, Eiron I, Cozzi A, et al. Graph-based Ranking Algorithms for E-mail Expertise Analysis[C]// Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery. 2003: 42-48.
[10]
Zhang J, Ackerman M S, Adamic L . Community Net Simulator: Using Simulations to Study Online Community Networks[A]// Steinfield C, Pentland B T, Ackerman M, et al. Communities and Technologies 2007[M]. Springer, 2007: 295-321.
[11]
Jurczyk P, Agichtein E. Discovering Authorities in Question Answer Communities by Using Link Analysis[C]// Proceedings of the 16th ACM Conference on Information and Knowledge Management, Lisbon, Portugal. 2007: 919-922.
[12]
Bouguessa M, Dumoulin B, Wang S. Identifying Authoritative Actors in Question-Answering Forums: The Case of Yahoo! Answers[C]// Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2008: 866-874.
[13]
Zhou G, Lai S, Liu K, et al. Topic-sensitive Probabilistic Model for Expert Finding in Question Answer Communities[C]// Proceedings of the 21st ACM International Conference on Information & Knowledge Management. 2012: 1662-1666.
[14]
戴秋敏 . 互动问答平台专家发现及问题推荐机制的研究[D]. 上海: 华东师范大学, 2014.
[14]
( Dai Qiumin . Research on Experts Finding and Question Recommendation Mechanism of User-interactive Q&A Platform[D]. Shanghai: East China Normal University, 2014.)
[15]
Dumais S T, Furnas G W, Landauer T K, et al. Using Latent Semantic Analysis to Improve Access to Textual Information[C]// Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 1988: 281-285.
[16]
Hofmann T. Probabilistic Latent Semantic Indexing[C]// Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 1999: 50-57.
[17]
Blei D M, Ng A Y, Jordan M I . Latent Dirichlet Allocation[J]. Journal of Machine Learning Research, 2003,3:993-1022.
[18]
Tian Y, Kochhar P S, Lim E P, et al. Predicting Best Answerers for New Questions: An Approach Leveraging Topic Modeling and Collaborative Voting[C]// Proceedings of the 2013 International Conference on Social Informatics. Springer, 2013: 55-68.
( Lin Hongfei, Wang Jian, Xiong Daping , et al. Category Participation-based Approach to Find Experts for Community Question Answer Services[J]. Computer Engineering and Design, 2014,35(1):333-338.)
[20]
Li H, Jin S, Li S. A Hybrid Model for Experts Finding in Community Question Answering[C]// Proceedings of the 2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery. IEEE, 2015: 176-185.
[21]
Cheng X, Zhu S, Chen G, et al. Exploiting User Feedback for Expert Finding in Community Question Answering[C]// Proceedings of the 2015 IEEE International Conference on Data Mining Workshop. IEEE, 2015: 295-302.
[22]
Blei D M, Lafferty J D. Correlated Topic Models[C]// Proceedings of the 18th International Conference on Neural Information Processing Systems. 2005: 147-154.
[23]
Li W, McCallum A. Pachinko Allocation: DAG-structured Mixture Models of Topic Correlations[C]// Proceedings of the 23rd International Conference on Machine Learning. ACM, 2006: 577-584.
[24]
Rosen-Zvi M, Griffiths T, Steyvers M , et al. The Author-Topic Model for Authors and Documents[OL]. arXiv Preprint, arXiv: 1207. 4169.
[25]
Guo X, Xiang Y, Chen Q , et al. LDA-based Online Topic Detection Using Tensor Factorization[J]. Journal of Information Science, 2013,39(4):459-469.
doi: 10.1177/0165551512473066
[26]
Ramage D, Hall D, Nallapati R, et al. Labeled LDA: A Supervised Topic Model for Credit Attribution in Multi-labeled Corpora[C]// Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing. 2009: 248-256.
( Yang Chunyan, Pan Youneng, Zhao Li . Study on Topic Extraction of Literatures Based on Weighted Semantic and Citation Relation[J]. Library and Information Service, 2016,60(9):131-138.)
[28]
Dai G, Xu M, Xu J , et al. Mining Bursty Topics from Twitter Text Streams Based on Labeled-LDA[J]. Journal of Computational Information Systems, 2014,10(11):4905-4912.
( Wang Shufeng, Wang Wen, Fei Xianju . An Personalized Recommendation Model Based on Context Information[J]. Journal of Changzhou Institute of Technology, 2014,27(2):27-31.)
[30]
Zhu X, Hao R, Chi H, et al. Personalized Location Recommendations with Local Feature Awareness[C]// Proceedings of the 2016 IEEE Global Communications Conference. IEEE, 2016.
( Lu Shengqi, Guan Lian, Jin Min , et al. The Application of LDA in Online Video Recommendation[J]. Microcomputer & Its Applications, 2016,35(11):74-79.)
( Zhu Yuxiao, Lü Linyuan . Evaluation Metrics for Recommender Systems[J]. Journal of University of Electronic Science and Technology of China, 2012,41(2):163-175.)