PageRank without hyperlinks: Structural re-ranking using links induced by language models
Oren Kurland and Lillian Lee
Proceedings of SIGIR, pp. 306--313, 2005. Journal version: Transactions on Information Systems (TOIS) 28(4), pp. 18:1--18:38, 2010

Inspired by the PageRank and HITS (hubs and authorities) algorithms for Web search, we propose a structural re-ranking approach to ad hoc information retrieval: we reorder the documents in an initially retrieved set by exploiting asymmetric relationships between them. Specifically, we consider generation links, which indicate that the language model induced from one document assigns high probability to the text of another; in doing so, we take care to prevent bias against long documents. We study a number of re-ranking criteria based on measures of centrality in the graphs formed by generation links, and show that integrating centrality into standard language-model-based retrieval is quite effective at improving precision at top ranks.

@inproceedings{Kurland+Lee:05a, author = {Oren Kurland and Lillian Lee}, title = {{PageRank} without hyperlinks: Structural re-ranking using links induced by language models}, year = {2005}, pages = {306--313}, booktitle = {Proceedings of SIGIR} }

language models and relevance: intuition

This paper is based upon work supported in part by the US National Science Foundation under grant no. IIS-0329064 and CCR-0122581; SRI International under subcontract no. 03-000211 on their project funded by the Department of the Interior’s National Business Center; and an Alfred P. Sloan Research Fellowship. Any opinions, findings, and conclusions or recommendations expressed are those of the authors and do not necessarily reflect the views or official policies, either expressed or implied, of any sponsoring institutions, the U.S. government, or any other entity