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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (9): 42-49    DOI: 10.11925/infotech.2096-3467.2018.0088
Current Issue | Archive | Adv Search |
Recommending Contents Based on Zhihu Q&A Community: Case Study of Logistics Topics
Yue He,Yue Feng,Shupeng Zhao(),Yufeng Ma
Business School, Sichuan University, Chengdu 610064, China
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[Objective] This research analyzes the social behaviors of Zhihu ( users, aiming to recommend relevant contents more effectively. [Methods] First, we proposed a content recommendation method based on association rules-LDA topic model. Then, we constructed a network of shared sub-topics for specific topics and extracted keywords of the sub-topics with the LDA model. Finally, we pushed contents of the relevant topics for the users. [Results] Our study found that many sub-topics with high degrees of cooccurrence under the topic of logistics, and their confidence levels were above 65%. [Limitations] More comprehensive data is needed in future studies.[Conclusions] The association rule-LDA model provides new directions for content recommendation.

Key wordsZhihu      Association Rule      LDA      Content Recommendation     
Received: 18 January 2018      Published: 25 October 2018

Cite this article:

Yue He,Yue Feng,Shupeng Zhao,Yufeng Ma. Recommending Contents Based on Zhihu Q&A Community: Case Study of Logistics Topics. Data Analysis and Knowledge Discovery, 2018, 2(9): 42-49.

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[1] 杨敏, 余小萍, 郑宏. 在线问答社区用户研究综述[J]. 图书馆学研究, 2014(14): 2-5.
[1] (Yang Min, Yu Xiaoping, Zheng Hong.Review of Online Q & A Community Users[J]. Library Science Research, 2014(14): 2-5.)
[2] 陈志明, 胡震云. UGC网站用户画像研究[J]. 计算机系统应用, 2017, 26(1): 24-30.
[2] (Chen Zhiming, Hu Zhenyun.User Portrait Study on UGC Website[J]. Computer Systems & Applications, 2017, 26(1): 24-30.)
[3] Shah C, Oh S, Oh J S.Research Agenda for Social Q&A[J]. Library& Information Science Research, 2009, 31(4): 205-209.
[4] Fan S X, Wang X L, Wang X, et al.Using Hybrid Kernel Method for Question Classification in CQA[C]//Proceedings of International Conference on Neural Information Processing. Berlin: Springer, 2011: 121-130.
[5] Qu B, Cong G, Li C P, et al.An Evaluation of Classification Models for Question Topic Categorization[J]. Journal of the American Society for Information Science and Technology, 2012, 63(5): 889-903.
[6] Chan W, Yang W, Tang J, et al.Community Question Topic Categorization via Hierarchical Kernelized Classification[C]// Proceedings of the 22nd ACM International Conference on Information & Knowledge Management. ACM, 2013: 959-968.
[7] 田野, 张静蓓. 基于词袋模型的关联数据融合算法改进研究[J]. 图书馆杂志, 2016(12): 17-22.
[7] (Tian Ye, Zhang Jingbei.Improvement of Linked Data Fusion Algorithm Based on Bag of Words[J]. Journal of Library Science, 2016(12): 17-22.)
[8] 李湘东, 霍亚勇, 张娇. 基于LDA主题模型的图书网页书目信息提取研究[J]. 情报科学, 2016, 34(1): 34-37.
[8] (Li Xiangdong, Huo Yayong, Zhang Jiao.Bibliographic Information Extraction Research of Book Pages Based on the LDA Theme Model[J]. Journal of Information Science, 2016, 34(1): 34-37.)
[9] Cai L, Zhou G, Liu K, et al.Large-scale Question Classification in CQA by Leveraging Wikipedia Semantic Knowledge[C]//Proceedings of the 20th ACM International Conference on Information and Knowledge Management. ACM, 2011: 1321-1330.
[10] Chang S, Pal A.Routing Questions for Collaborative Answering in Community Question Answering[C]// Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. ACM, 2013: 494-501.
[11] Wu H, Wu W, Zhou M, et al.Improving Search Relevance for Short Queries in Community Question Answering[C]// Proceedings of the 7th ACM International Conference on Web Search and Data Mining. ACM, 2014: 43-52.
[12] Paul S A, Hong L, Chi E H.Who is Authoritative? Understanding Reputation Mechanisms in Quora[C]// Proceedings of Collective Intelligence, 2012.
[13] Chen Y, Ho T, Kim Y.Knowledge Market Design: A Field Experiment at Google Answers[J]. Journal of Public Economic Theory, 2010, 12(4): 641-664.
[14] Gazan R.Seekers, Sloths and Social Reference: Homework Questions Submitted to a Question-Answering Community[J]. New Review of Hypermedia and Multimedia, 2007, 13(2): 239-248.
[15] 黄维, 赵鹏. 虚拟社区用户知识共享行为影响因素研究[J]. 情报科学, 2016, 34(4): 68-73.
[15] (Huang Wei, Zhao Peng.Exploring Influencing Factors of User Knowledge Sharing Behavior in Virtual Communities[J]. Journal of Information Science, 2016, 34(4): 68-73.)
[16] Jurczyk P, Agichtein E.Discovering Authorities in Question Answer Communities Using Link Analysis[C]//Proceedings of the 16th ACM International Conference on Information and Knowledge Management. ACM, 2007: 919-922.
[17] Gazan R.Microcollaborations in a Social Q&A Community[J]. Information Processing and Management, 2010, 46(6): 693-702.
[18] 马炎. 一种自适应的协作过滤图书推荐系统研究[J]. 情报杂志, 2008, 27(5):105-106,109.
[18] (Ma Yan.Research on the Adaptive Collaborative Filtering Recommendation System[J]. Journal of Information, 2008, 27(5): 105-106, 109.)
[19] IJntema W, Goossen F, Frasincar F, et al.Ontology-Based News Recommendation[C]//Proceedings of the 1st International Workshop on Data Semantics, Switzerland. 2010: 22-26.
[20] Wu H, Yue K, Pei Y, et al.Collaborative Topic Regression with Social Trust Ensemble for Recommendation in Social Media Systems[J]. Knowledge-Based Systems, 2016, 97(1): 111-122.
[21] Kim Y,Shim K.TWILITE: A Recommendation System for Twitter Using a Probabilistic Model Based on Latent Dirichlet Allocation[J]. Information Systems, 2014, 42(3): 59-77.
[22] 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, Singapore. ACL, 2009: 248-256.
[23] Wang X, McCallum A. Topics over Time: A Non-Markov Continuous-time Model of Topical Trends[C]//Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2006: 424-433.
[24] Blei D M, Ng A Y, Jordan M I.Latent Dirichlet Allocation[J]. Journal of Machine Learning Research, 2003, 3: 993-1022.
[25] 艾丹祥, 张玉峰, 左晖, 等. 面向C2C在线情景的一种个性化三维推荐方法[J]. 情报学报, 2016, 35(6): 651-663.
[25] (Ai Danxiang, Zhang Yufeng, Zuo Hui, et al.A Personalized Three-dimensional Recommendation Method for C2C Online Context[J]. Journal of the China Society for Scientific and Technical Information, 2016, 35(6): 651-663.)
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