An online blog reading system by topic clustering and personalized ranking

ACM Transactions on Internet Technology

Volumn 9, Issue 3, 2009, Pages

  Li, Xin ^a   Yan, Jun ^b   Fan, Weiguo ^c   Liu, Ning ^b   Yan, Shuicheng ^d   Chen, Zheng ^b  

^a PEKING UNIVERSITY   (China)

^b MICROSOFT RESEARCH ASIA   (China)

^c VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

^d NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Blog; Connected subgraph; Content information; Link information; Personalization; Ranking; Story; Topic

Indexed keywords

BLOG; CONNECTED SUBGRAPH; CONTENT INFORMATION; LINK INFORMATION; PERSONALIZATION; RANKING; STORY; TOPIC;

CLUSTERING ALGORITHMS; CONTENT BASED RETRIEVAL; GRAPH THEORY; WINDOWS;

INTERNET;

EID: 69149090858     PISSN: 15335399     EISSN: 15576051     Source Type: Journal    
DOI: 10.1145/1552291.1552292     Document Type: Article

References (49)

1. Adar, E., Zhang, L., Adamic, L. A., and Lukose, R. M. 2004. Implicit structure and the dynamics of blogspace. In Proceedings of the 13th International World Wide Web Conference Workshop on the Weblogging Ecosystem. 35-39.
2. Allan, J., Papka, R., and Lavrenko, V. 1998. Online new event detection and tracking. In Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 37-45.
3. Avesani, P., Cova, M., Hayes, C., and Massa, P. 2005. Learning contextualised Weblog topics. In Proceedings of the 2nd Annual Workshop on the Weblogging Ecosystem: Aggregation, Analysis and Dynamics, E. Adar, N. Glance, and M. Hurst, Eds.
4. Baker, L. D. and McCallum, A. K. 1998. Distributional clustering of words for text classification. In Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 96-103.
5. Balabanovich, M. and Shoham, Y. 1997. Fab: Content-based, collaborative recommendation. Comm. ACM, 40, 3, 66-72.
6. Bansal, N., Blum, A., and Chawla, S. 2004. Correlation clustering. Machine Learn. 3, 89-113.
7. Bansal, N., Chiang, F., Koudas, N., and Tompa, W. F. 2007. Seeking stable clusters in the blogosphere. In Proceedings of the 33rd International Conference on Very Large Databases. 806-817.
8. Bansal, N. and Koudas, N. 2007. BLOGSCOPE: A system for online analysis of high volume text streams. In Proceedings of the 33rd International Conference on Very Large Databases. 1410-1413.
9. Bern, M. and Eppstein, D. 1996. Approximation algorithms for geometric problems. In Approximation Algorithms for NP-Hard Problems, D. S. Hochbaum, Ed. PWS Publishing Company, Boston, 296-345.
10. Bonett, M. 2001. Personalization of Web services: Opportunities and challenges. Ariadne 28.]]
11. Brants, T. and Chen, F. R. 2003. A system for new event detection. In Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information. 330-337.
12. Brooks, C. H. and Andmontanez, N. 2005. An analysis of the effectiveness of tagging in blogs. In AAAI Spring Symposium on Computational Approaches to Analyzing Weblogs, vol. 4737, 1-20.
13. Cayzer, S. 2004. Semantic blogging and decentralized knowledge management. Comm. ACM 47, 12, 47-52.
14. Chen, J. L., Yan, J., Zhang B. Y., Yang, Q., and Chen, Z. 2006. Diverse topic phrase extraction through latent semantic analysis. In Proceedings of the 6th International Conference on Data Mining. 834-838.
15. Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., and Harshman, R. 1990.Indexing by latent semantic analysis. J. Amer. Soc. Inform. Sci. 41, 6, 391-407.
16. Delwiche, A. 2005. Agenda-setting, opinion leadership, and the world of Weblogs. First Monday, 10, 12.
17. Fan, W. G., Gordon, M. D., and Pathak, P. 2004. Discovery of context-specific ranking functions for effective information retrieval by Genetic Programming. IEEE Trans. Knowl. Data Eng. 16, 4, 523-527.
18. Fan, W. G., Gordon, M. D., and Pathak, P. 2005. Genetic programming-based discovery of ranking functions for effective Web search. J. Manag. Inform. Syst. 21, 4, 37-56.
19. Ferragina, P. and Gulli, A. 2005. A personalized search engine based on Web-snippet hierarchical clustering. In Proceedings of the 14th International Conference on the World Wide Web Special Interest Tracks and Posters. 801-810.
20. Gibson, D., Kleinberg, J., and Raghavan, P. 1998. Inferring Web communities from link topology. In Proceedings of the 9th Conference on Hypertext and Hypermedia. 225-234.
21. Giotis, I. and Guruswami, V. 2006. Correlation clustering with a fixed number of clusters. In Proceedings of the ACM Symposium on Discrete Algorithms, 1167-1176.
22. Gordon, M. D., Fan, W. G., and Pathak, P. 2006. Adaptive Web search: Evolving a program that finds information. IEEE Intell. Syst. 21, 5, 72-77.
23. Hayes, C., Avesani, P., and Veeramachaneni, S. 2006a. An analysis of the use of tags in a blog recommender system. ITC-IRST Tech. rep., IJCAI: 2772-2777.
24. Hayes, C., Avesani, P., and Veeramachaneni, S. 2006b. An analysis of bloggers and topics for a blog recommender system. In Proceedings of the 7th European Conference on Machine Learning and the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) Workshop on Web Mining.
25. Herring, S. C., Kouper, I., Paolillo, J. C., Scheidt, L. A., Tyworth, M., Welsch, P., Wright, E., and Yu, N. 2005. Conversations in the blogosphere: An analysis from the bottom up. In Proceedings of the 38th Hawaii International Conference on System Sciences. 1530-1605.
26. Jeh, G. and Widom, J. 2003. Scaling personalized Web search. In Proceedings of the 12th International Conference on World Wide Web. 271-279.
27. Kantrowitz, M., Behrang, M., and Mittal, V. 2000. Stemming and its effects on TFIDF Ranking. In Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 357-359.
28. Karger, D. R. and Quan, D. 2005. What would it mean to blog on the semantic Web. Web In Semantics: Science, Services and Agents on the World Wide Web, vol. 3, 147-157.
29. Karypis, G. and Kumar, V. 1998. Multi-level k-way partitioning scheme for irregular graphs. J. Parall. Distrib. Comput., vol. 48, 96-129.
30. Kelleher, J. and Bridge, D. 2004. An accurate and scalable collaborative recommender. Artif. Intell. Rev. 21, 3-4, 193-213.
31. Kleinberg, J. 1999. Authoritative sources in a hyperlinked environment. J. ACM 46, 5, 604-632.
32. Koller, D. and Sahami, M. 1997. Hierarchically classifying documents using very few words. In Proceedings of the 14th International Conference on Machine Learning. 170-178.
33. Kumar, R., Novak, P., Raghavan, S., and Tomkins, A. 2003. On the bursty evolution of blogspace. In Proceedings of the 12th International Conference on World Wide Web. 159-178.
34. Liu, F., Yu, C., and Meng, W. Y. 2004. Personalized Web search for improving retrieval
35. effectiveness. IEEE Trans. Knowl. Data Eng. 16, 1, 28-40.
36. Mancoridis, S., Mitchell, B., Rorres, C., Chen, Y., and Gansner, E. 1998. Using automatic clustering to produce high-level system organizations of source code. In Proceedings of the 6th International Workshop on Program Comprehension. 45-53.
37. Marlow, C. 2004. Audience, structure and authority in the Weblog community. In Proceedings of the International Communication Association Conference.
38. Page, L., Brin, S., Motwani, R., and Winograd, T. 1998. The PageRank citation ranking: Bringing order to the Web. Tech. rep. Stanford University.
39. Qiu, F. and Cho, J. 2006. Automatic identification of user interest for personalized search. In Proceedings of the 15th International Conference on World Wide Web. 727-736.
40. Quintarelli, E. 2005. Folksonomies: Power to the people. ISKO Italy-UniMIB Meeting.
41. Rand, W. M. 1971. Objective criteria for the evaluation of clustering methods. J. Amer. Statis. Assoc. 66, 336, 846-850.
42. Salton, G., and McGill, M. J. 1983. An Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York.
43. Sarwar, B. M., Karypis, G., Konstan, J., and Riedl, J. 2002. Recommender systems for large-scale e-commerce: Scalable neighborhood formation using clustering. In Proceedings of the 5th International Conference on Computer and Information Technology.
44. Singhal, A. and Salton, G. 1995. Automatic text browsing using vector space model. In Proceedings of the 5th Dual-Use Technologies and Applications Conference. 318-324.
45. Solomonoff, A., Mielke, A., Schmidt, M., and Gish, H. 1998. Clustering speakers by their voices. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing. 757-760.
46. Wu, Z. and Leahy, R. 1993. An optimal graph theoretic approach to data clustering: Theory and its application to image segmentation. IEEE Trans. Patt. Anal. Mach. Intell. 15, 11, 1101-1113.
47. Tsai, T.-M., Shih, C.-C., and Chou, S.-C. T. 2006. Personalized blog recommendation
48. using the value, semantic, and social model. In Innovations in Information Technology. 1-5.
49. Yang, Y., Pierce, T., and Carbonell. J. G. 1998. A study on retrospective and online event detection. In Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 28-36.