Using probabilistic topic models in enterprise social software

Lecture Notes in Business Information Processing

Volumn 47 LNBIP, Issue , 2010, Pages 23-34

  Christidis, Konstantinos ^a   Mentzas, Gregoris ^a  

^a NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

Author keywords

Enterprise social software; Latent dirichlet allocation; Probabilistic topic models; Recommender systems

Indexed keywords

INFORMATION SYSTEMS; RECOMMENDER SYSTEMS; SEMANTIC WEB; STATISTICS;

COLLECTIVE STRUCTURES; CORPORATE ENVIRONMENT; INFORMAL INTERACTIONS; LATENT DIRICHLET ALLOCATION; PROBABILISTIC TOPIC MODELS; SOCIAL SOFTWARE; TAG RECOMMENDATIONS; WEB 2.0 TECHNOLOGIES;

INDUSTRY;

EID: 84859212215     PISSN: 18651348     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-12814-1_3     Document Type: Conference Paper

References (32)

1. Reilly, T.: What is Web 2.0: Design patterns and business models for the next generation of software
2. McAfee, A.P.: Enterprise 2.0: The dawn of emergent collaboration. MIT Sloan Management Review 47, 21 (2006)
3. Eid, T.: The Enterprise Social Software Market Continues to Expand Gartner (2008)
4. Gilchrist, A.: Corporate taxonomies: report on a survey of current practice. Online Information Review 25, 94-103 (2001)
5. Folksonomy Coinage and Definition, http://www.vanderwal.net/folksonomy. html
6. Shirky, C., Butterfield, S., et al.: Folksonomy, or How I Learned to Stop Worrying and Love the Mess. (2005)
7. Dotsika, F.: Uniting formal and informal descriptive power: Reconciling ontologies with folksonomies. International Journal of Information Management 29, 407-415 (2009)
8. Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of the 22nd ACM SIGIR conference on Research and development in information retrieval, pp. 50-57 (1999)
9. Steyvers, M., Griffiths, T.: Probabilistic topic models. In: Handbook of Latent Semantic Analysis, pp. 424-440 (2007)
10. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993-1022 (2003)
11. Steyvers, M., et al.: Probabilistic author-topic models for information discovery. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 306-315 (2004) doi:10.1145/1014052. 1014087 (Pubitemid 40114940)
12. Shilakes, C.C., Tylman, J.: Enterprise information portals. Merrill Lynch (November 16, 1998)
13. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering 17, 734-749 (2005) (Pubitemid 40860454)
14. Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. Internet Computing, IEEE 7, 76-80 (2003)
15. Schafer, J.B., Konstan, J., Riedi, J.: Recommender systems in e-commerce. In: Proceedings of the 1st ACM conference on Electronic commerce, pp. 158-166 (1999)
16. Jin, X., Zhou, Y., Mobasher, B.: Web usage mining based on probabilistic latent semantic analysis. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 197-205 (2004) doi:10.1145/1014052.1014076 (Pubitemid 40114929)
17. Chen, W.Y., et al.: Collaborative filtering for orkut communities: discovery of user latent behavior. In: Proceedings of the 18th international conference on World wide web, pp. 681-690 (2009)
18. Haruechaiyasak, C., Damrongrat, C.: Article Recommendation Based on a Topic Model for Wikipedia Selection for Schools. Digital Libraries: Universal and Ubiquitous Access to Information, 339-342 (2008)
19. Schmitz, C., et al.: Mining Association Rules in Folksonomies. Data Science and Classification, 261-270 (2006), http://dx.doi.org/10.1007/3-540- 34416-0-28
20. Hotho, A., et al.: Information retrieval in folksonomies: Search and ranking
21. Krestel, R., Fankhauser, P., Nejdl, W.: Latent dirichlet allocation for tag recommendation. In: Proceedings of the third ACM conference on Recommender systems, pp. 61-68 (2009)
22. Voorhees, E.M.: Query expansion using lexical-semantic relations. In: Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 61-69 (1994), http://portal.acm. org/citation.cfm?id=188508
23. Xu, J., Croft, W.B.: Query expansion using local and global document analysis. In: Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 4-11 (1996)
24. Park, L.A., Ramamohanarao, K.: The sensitivity of latent dirichlet allocation for information retrieval. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS, vol. 5782, pp. 176-188. Springer, Heidelberg (2009)
25. Uschold, M., Gruninger, M.: Ontologies and semantics for seamless connectivity. SIGMOD Rec. 33, 58-64 (2004) (Pubitemid 40056334)
26. Lin, J.: Divergence measures based on the Shannon entropy. IEEE Transactions on Information theory 37, 145-151 (1991)
27. Bibikas, D., et al.: A Sociotechnical Approach to Knowledge Management in the Era of Enterprise 2.0: The case of OrganiK. Scalable Computing: Practice and Experience 9, 315-327 (2008)
28. McCallum, A.K.: MALLET: A Machine Learning for Language Toolkit (2002), http://mallet.cs.umass.edu
29. Passant, A., et al.: Semantic Search for Enterprise 2.0
30. Ehrlich, K., Lin, C., Griffiths-Fisher, V.: Searching for experts in the enterprise: combining text and social network analysis. In: Proceedings of the 2007 international ACM conference on Supporting group work, pp. 117-126 (2007)
31. Dmitriev, P.A., et al.: Using annotations in enterprise search. In: Proceedings of the 15th international conference on World Wide Web, pp. 811-817 (2006) (Pubitemid 46946676)
32. Zhen, L., Huang, G.Q., Jiang, Z.: An inner-enterprise knowledge recommender system. Expert Systems with Applications 37, 1703-1712 (2010)