A probabilistic framework of preference discovery from folksonomy corpus

Abstract

The increasing availability of folksonomy data makes them vital for user profiling approaches to precisely detect user preferences and better understand user interests, so as to render some personalized recommendation or retrieval results. This paper presents a rigorous probabilistic framework to discover user preference from folksonomy data. Furthermore, we incorporate three models into the framework with the corresponding inference methods, expectation-maximization or Gibbs sampling algorithms. The user preference is expressed through topical conditional distributions. Moreover, to demonstrate the versatility of our framework, a recommendation method is introduced to show the possible usage of our framework and evaluate the applicability of the engaged models. The experimental results show that, with the help of the proposed framework, the user preference can be effectively discovered.

Keywords

preference discovery / tagging / folksonomy / social annotation

References

[1] SenS, VigJ, RiedlJ. Tagommenders: connecting users to items through tags. In: Proceedings of the 18th International Conference on World Wide Web. 2009, 671–680

[2] WetzkerR, Zimmermann C, BauckhageC , AlbayrakS. I tag, you tag: translating tags for advanced user models. In: Proceedings of the 3rd ACMInternational Conference onWeb Search and DataMining. 2010, 71–80

[3] LiangH, XuY, LiY, NayakR, TaoX. Connecting users and items with weighted tags for personalized item recommendations. In: Proceedings of the 21st ACM Conference on Hypertext and Hypermedia. 2010, 51–60

[4] HofmannT. Probabilistic latent semantic indexing. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 1999, 50–57

[5] BleiD M, NgA Y, JordanM I, Lafferty J. Latent Dirichlet allocation. Journal of Machine Learning Research, 2003, 3: 993–1022

[6] Rosen-ZviM, Griffiths T L, SteyversM , SmythP. The author-topic model for authors and documents. In: Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence. 2004, 487–494

[7] HalpinH, RobuV, ShepherdH. The complex dynamics of collaborative tagging. In: Proceedings of the 16th International Conference on World Wide Web. 2007, 211–220

[8] MarkinesB, Cattuto C, MenczerF , BenzD, HothoA, StummeG. Evaluating similarity measures for emergent semantics of social tagging. In: Proceedings of the 18th International Conference onWorld Wide Web. 2009, 641–650

[9] XuH, WangJ, HuaX S, Li S. Tag refinement by regularized LDA. In: Proceedings of the 17th ACM International Conference onMultimedia. 2009, 573–576

[10] YinD W, XueZ Z, HongL J, Davison B D. A probabilistic model for personalized tag prediction. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2010, 959–968

[11] BelémF, Martins E, PontesT , AlmeidaJ, Gonçalves M. Associative tag recommendation exploiting multiple textual features. In: Proceedings of the 34th International ACM SIGIR Conference on Research andDevelopment in Information Retrieval. 2011, 1033–1042

[12] EdaT, Yoshikawa M, UchiyamaT , UchiyamaT. The effectiveness of latent semantic analysis for building up a bottom-up taxonomy from folksonomy tags. World Wide Web, 2009, 12: 421–440

[13] DaudA, LiJ Z, ZhouL Z, Zhang L, DingY , MuhammadF. Modeling ontology of folksonomy with latent semantics of tags. In: Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. 2010, 516–523

[14] SunA, Bhowmick S S. Image tag clarity: in search of visualrepresentative tags for social images. In: Proceedings of the 1st SIGMM Workshop on Social Media. 2009, 19–26

[15] LinY, LinH F, JinS, Ye Z. Social annotation in query expansion: a machine learning approach. In: Proceedings of the 34th International ACMSIGIR Conference on Research and Development in Information Retrieval. 2011, 405–414

[16] XuS, BaoS, FeiB, Su Z, YuY . Exploring folksonomy for personalized search. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2008, 155–162

[17] SymeonidisP, Nanopoulos A, ManolopoulosY . A unified framework for providing recommendations in social tagging systems based on ternary semantic analysis. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(2): 179–192

[18] CaiY, LiQ. Personalized search by tag-based user profile and resource profile in collaborative tagging systems. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management. 2010, 969–978

[19] BrafmanR I, Domshlak C. Preference handling — an introductory tutorial. AI Magazine, 2009, 30(1): 58–86

[20] FürrnkranzJ, Hürllermeier E. Preference Learning: An Introduction. New York: Springer, 2011

[21] MinkaT, Lafferty J. Expectation-propagation for the generative aspect model. In: Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence. 2002, 352–359

[22] GriffithsT L, Steyvers M. Finding scientific topics. In: Proceedings of the National Academy of Sciences. 2004, 5228–5235

RIGHTS & PERMISSIONS

2016 Higher Education Press and Springer-Verlag Berlin Heidelberg