The proliferation of knowledge-sharing communities has generated large amounts of data. Prominent examples of how user-generated content can be harnessed include IBM’s Watson question answering sytem and Apple’s Siri, the question answering application in iPhones. Facing such massive data, user authority ranking is important to the development of question answering and other e-commerce services. In this study, we propose three probabilistic models to rank the user authority of each question. Compared to the existing approaches focused on the user relationship primarily, our method is more effective because we consider the link structure and topical similarities between users and questions simultaneously. We use a real-world dataset from Zhihu, a popular community question answering website in China to conduct experiments. Experimental results show that our model outperforms other baseline methods in ranking the user authority.
LiuX., YeS., LiX., LuoY. and RaoY., ZhihuRank: A topic-sensitive expert finding algorithm in community question answering websites, in Proceedings of the 14th International Conference on Web-based Learning (ICWL), 2015, pp. 165–173.
2.
ChenH., ChiangR.H.L. and StoreyV.C., Business intelligence and analytics: From big data to big impact, MIS Quarterly36(4) (2012), 1–24.
3.
LiuY., LiS., CaoY., LinC.-Y., HanD. and YuY., Understanding and summarizing answers in community-based question answering services, in Proceedings of the 22nd International Conference on Computational Linguistics (Coling), 2008, pp. 497–504.
4.
AgichteinE., CastilloC., DonatoD., GionisA. and Mishne.G., Finding high-quality content in social media, in Proceedings of the 1st ACM International Conference on Web Search and Data Mining (WSDM), 2008, pp. 183–194.
5.
BrinS. and PageL., The anatomy of a large-scale hypertextual web search engine, Computer Network and ISDN Systems30(1-7) (1998), 107–117.
6.
KleinbergJ.M., Authoritative sources in a hyperlinked environment, Journal of the ACM46(5) (1999), 604–632.
7.
BleiD.M., NgA.Y. and JordanM.I., Latent dirichlet allocation, Journal of Machine Learning Research3 (2003), 993–1022.
8.
BouguessaM., DumoulinB. and WangS., Identifying authoritative actors in question-answering forums: The case of yahoo! answers, in Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), 2008, pp. 866–874.
9.
JurczykP. and AgichteinE., Discovering authorities in question answer communities by using link analysis, in Proceedings of the ACM 16th Conference on Information and Knowledge Management (CIKM), 2007, pp. 919–922.
10.
ZhangJ., AckermanM.S. and AdamicL., Expertise networks in online communities: Structure and algorithms, in Proceedings of the 16th International World Wide Web Conference (WWW), 2007, pp. 221–230.
11.
GuoJ., XuS., BaoS. and YuY., Tapping on the potential of QA community by recommending answer providers, in Proceedings of the 17th ACM Conference on Information and Knowledge Management (CIKM), 2008, pp. 921–930.
12.
LiuX., CroftW.B. and KollM., Finding experts in community-based question answering services, in Proceedings of the 2005 ACM CIKM International Conference on Information and Knowledge Management (CIKM), 2005, pp. 315–316.
13.
JeonJ., CroftW.B. and LeeJ.H., Finding similar questions in large question and answer archives, in Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM), 2005, pp. 84–90.
14.
LiS. and ManandharS., Improving question recommendation by exploiting information need, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT), 2011, pp. 1425–1434.
15.
HaoT. and AgichteinE., Finding similar questions in collaborative question answering archives: Toward bootstrapping-based equivalent pattern learning, Information Retrieval15(3-4) (2012), 332–353.
16.
WuH., WangY. and ChengX., Incremental probabilistic latent semantic analysis for automatic question recommendation, in Proceedings of the 2nd ACM Conference on Recommender Systems (RecSys), 2008, pp. 99–106.
17.
QuM., QiuG., HeX., ZhangC., WuH., BuJ. and ChenC., Probabilistic question recommendation for question answering communities, in Proceedings of the 18th International World Wide Web Conference (WWW), 2009, pp. 1229–1230.
18.
WengJ., LimE.-P., JiangJ. and HeQ., Twitterrank: Finding topic-sensitive influential twitterers, in Proceedings of the 3rd International Conference on Web Search and Data Mining (WSDM), 2010, pp. 261–270.
19.
ZhouG., LaiS., LiuK. and ZhaoJ., Topic-sensitive probabilistic model for expert finding in question answer communities, in Proceedings of the 21st ACM Conference on Information and Knowledge Management (CIKM), 2012, pp. 1662–1666.
20.
HaveliwalaT.H., Topic-sensitive pagerank, in Proceedings of the 11th International World Wide Web Conference (WWW), 2002, pp. 517–526.
21.
ZhaoT., BianN., LiC. and LiM., Topic-level expert modeling in community question answering, in SIAM International Conference on Data Mining (SDM), 2013, pp. 776–784.
22.
ChenB.-C., GuoJ., TsengB. and YangJ., User reputation in a comment rating environment, in Proceedings of the 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2011, pp. 159–167.
23.
PalA. and CountsS., Identifying topical authorities in microblogs, in Proceedings of the 4th ACM International Conference on Web Search and Data Mining (WSDM), 2011, pp. 45–54.
24.
LiuY., QiuM., GottipatiS., ZhuF., JiangJ., SunH. and ChenZ., CQARank: Jointly model topics and expertise in community question answering, in Proceedings of the 22nd International Conference on Information and Knowledge Management (CIKM), 2013, pp. 99–108.
25.
FerrucciD., BrownE., Chu-CarrollJ., FanJ., GondekD., KalyanpurA. and SchlaeferN., Building Watson: An overview of the DeepQA project, AI magazine31(3) (2010), 59–79.
26.
UngerC., BühmannL., LehmannJ., NgomoN., GerberA.C. and CimianoP., Template-based question answering over RDF data, in Proceedings of the 21st International World Wide Web Conference (WWW), 2012, pp. 639–648.
27.
LopezV., UngerC., CimianoP. and MottaE., Evaluating question answering over linked data, Web Semantics: Science, Services and Agents on the World Wide Web21 (2013), 3–13.
28.
MarxE., UsbeckR., NgomoA.C.N., HöffnerK., LehmannJ. and AuerS., Towards an open question answering architecture, in Proceedings of the 10th International Conference on Semantic Systems (SEM), 2014, pp. 57–60.
29.
ZouL., HuangR., WangH., YuJ.X., HeW. and ZhaoD., Natural language question answering over RDF: A graph data driven approach, in Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data (SIGMOD), 2014, pp. 313–324.
30.
AntolS., AgrawalA., LuJ., MitchellM., BatraD. and LawrenceC., Zitnick and D. Parikh, VQA: Visual question answering, in Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 2425–2433.
31.
RenM., KirosR. and ZemelR., Exploring models and data for image question answering, in Advances in Neural Information Processing Systems (NIPS), 2015, pp. 2935–2943.
32.
MalinowskiM., RohrbachM. and FritzM., Ask your neurons: A neural-based approach to answering questions about images, in Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1–9.
33.
GemanS. and GemanD., Stochastic relaxation: Gibbs distributions, and the Bayesian restoration of images, IEEE Transactions on Pattern Analysis and Machine Intelligence6(6) (1984), 721–741.
34.
NorrisJ.R., Markov chains, Cambridge University Press, 1998.
35.
GriffithsT. and SteyversM., Finding scientific topics, Proceedings of the National Academy of Sciences of the United States of America101 (2004), 5228–5235.
36.
PanZ., ZhangY. and KwongS., Efficient motion and disparity estimation optimization for low complexity multiview video coding, IEEE Transactions on Broadcasting61(2) (2015), 166–176.
37.
ZhengY., JeonB., XuD., WuQ.M. and ZhangH., Image segmentation by generalized hierarchical fuzzy C-means algorithm, Journal of Intelligent and Fuzzy Systems28(2) (2015), 961–973.
38.
LiJ., LiX., YangB. and SunX., Segmentation-based image copy-move forgery detection scheme, IEEE Transactions on Information Forensics and Security10(3) (2015), 507–518.
39.
XieH., LiQ., MaoX., LiX., CaiY. and ZhengQ., Mining latent user community for tag-based and content-based search in social media, Computer Journal57(9) (2014), 1415–1430.
40.
XieH., LiQ. and CaiY., Community-Aware resource profiling for personalized search in folksonomy, Journal of Computer Science and Technology27(3) (2012), 599–610.
41.
LiX., XieH., ChenL., WangJ. and DengX., News impact on stock price return via sentiment analysis, Knowledge-Based Systems69 (2014), 14–23.
42.
LiX., XieH., SongY., ZhuS., LiQ. and WangF.L., Does summarization help stock prediction? a news impact analysis, IEEE Intelligent Systems30(3) (2015), 26–34.
43.
RaoY., LeiJ., LiuW., LiQ. and ChenM., Building emotional dictionary for sentiment analysis of online news, World Wide Web17(4) (2014), 723–742.
44.
LeiJ., RaoY., LiQ., QuanX. and LiuW., Towards building a social emotion detection system for online news, Future Generation Computer Systems37 (2014), 438–448.
45.
RaoY., Contextual sentiment topic model for adaptive social emotion classification, IEEE Intelligent Systems31(1) (2016), 41–47.
46.
RaoY., LiQ., LiuW., WuQ. and QuanX., Affective topic model for social emotion detection, Neural Networks58 (2014), 29–37.
47.
RaoY., LiQ., MaoX. and LiuW., Sentiment topic models for social emotion mining, Information Sciences266 (2014), 90–100.
48.
XieH., ZouD., LauR., WangF.L. and WongT.L., Generating incidental word learning tasks via topic-based and load-based profiles, IEEE MultiMedia23(1) (2016), 60–70.
49.
XieH., LiX., WangT., LauR.Y.K., WongT.L., ChenL., WangF.L. and LiQ., Incorporating sentiment into tag-based user profiles and resource profiles for personalized search in folksonomy, Information Processing and Management52(1) (2016), 61–72.
50.
XieH., LiQ., MaoX., LiX., CaiY. and RaoY., Communty-aware user profile enrichment in folksonomy, Neural Networks58 (2014), 111–121.
51.
WenX., ShaoL., XueY. and FangW., A rapid learning algorithm for vehicle classification, Information Sciences295(1) (2015), 395–406.
52.
GuB., ShengV.S., WangZ., HoD., OsmanS. and LiS., Incremental learning for ν-support vector regression, Neural Networks67 (2015), 140–150.
53.
GuB. and ShengV.S., A robust regularization path algorithm for ν-support vector classification, IEEE Transactions on Neural Networks and Learning Systems (2016). DOI: 10.1109/TNNLS.2016.2527796
