Web page scoring systems for horizontal and vertical search

ABSTRACT

Page ranking is a fundamental step towards the construction of effective search engines for both generic (horizontal) and focused (vertical) search. Ranking schemes for horizontal search like the PageRank algorithm used by Google operate on the topology of the graph, regardless of the page content. On the other hand, the recent development of vertical portals (vortals) makes it useful to adopt scoring systems focussed on the topic and taking the page content into account.In this paper, we propose a general framework for Web Page Scoring Systems (WPSS) which incorporates and extends many of the relevant models proposed in the literature. Finally, experimental results are given to assess the features of the proposed scoring systems with special emphasis on vertical search.

REFERENCES

• 1: Monika R. Henzinger, Hyperlink Analysis for the Web, IEEE Internet Computing, v.5 n.1, p.45-50, January 2001 [doi>10.1109/4236.895141]
• 2: L. Page, S. Brin, R. Motwani, and T. Winograd, "The PageRank citation ranking: Bringing order to the web," tech. rep., Computer Science Department, Stanford University, 1998.
• 3: J. Kleinberg, "Authoritative sources in a hyperlinked environment." Report RJ 10076, IBM, May 1997, 1997.
• 4: Krishna Bharat , Monika R. Henzinger, Improved algorithms for topic distillation in a hyperlinked environment, Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, p.104-111, August 24-28, 1998, Melbourne, Australia [doi>10.1145/290941.290972]
• 5: R. Lempel , S. Moran, The stochastic approach for link-structure analysis (SALSA) and the TKC effect, Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking, p.387-401, June 2000, Amsterdam, The Netherlands
• 6: R. Lempel , S. Moran, SALSA: the stochastic approach for link-structure analysis, ACM Transactions on Information Systems (TOIS), v.19 n.2, p.131-160, April 2001 [doi>10.1145/382979.383041]
• 7: David Cohn , Huan Chang, Learning to Probabilistically Identify Authoritative Documents, Proceedings of the Seventeenth International Conference on Machine Learning, p.167-174, June 29-July 02, 2000
• 8: D. Cohn and T. Hofmann, "The missing link: a probabilistic model of document content and hypertext connectivity," in Neural Information Processing Systems, vol. 13, 2001.
• 9: E. Seneta, Non-negative matrices and Markov chains. Springer-Verlag, 1981.
• 10: M. Joshi, V. Tawde, and S. Chakrabarti, "Enhanced topic distillation using text, markup tags, and hyperlinks," in International ACM Conference on Research and Development in Information Retrieval (SIGIR), August 2001.
• 11: Michelangelo Diligenti , Frans Coetzee , Steve Lawrence , C. Lee Giles , Marco Gori, Focused Crawling Using Context Graphs, Proceedings of the 26th International Conference on Very Large Data Bases, p.527-534, September 10-14, 2000
• 12: Brian Amento , Loren Terveen , Will Hill, Does “authority” mean quality? predicting expert quality ratings of Web documents, Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, p.296-303, July 24-28, 2000, Athens, Greece [doi>10.1145/345508.345603]

INDEX TERMS

Primary Classification:
  F. Theory of Computation
  F.2 ANALYSIS OF ALGORITHMS AND PROBLEM COMPLEXITY
      F.2.2 Nonnumerical Algorithms and Problems
          Subjects: Sorting and searching

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Information filtering
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.4 Hypertext/Hypermedia

General Terms:
Algorithms

Keywords:
Focused PageRank, HITS, PageRank, random walks, web page scoring systems