2010 FastQueryExecutionforRetrievalM

• (Lao et al., 2010) ⇒ Ni Lao, and William W. Cohen. (2010). “Fast Query Execution for Retrieval Models based on Path-constrained Random Walks.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835916

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Cited By

• http://scholar.google.com/scholar?q=%22Fast+query+execution+for+retrieval+models+based+on+path-constrained+random+walks%22+2010
• http://portal.acm.org/citation.cfm?id=1835916&preflayout=flat#citedby

Quotes

Abstract

Many recommendation and retrieval tasks can be represented as proximity queries on a labeled directed graph, with typed nodes representing documents, terms, and metadata, and labeled edges representing the relationships between them. Recent work has shown that the accuracy of the widely-used random-walk-based proximity measures can be improved by supervised learning - in particular, one especially effective learning technique is based on Path-Constrained Random Walks (PCRW), in which similarity is defined by a learned combination of constrained random walkers, each constrained to follow only a particular sequence of edge labels away from the query nodes. The PCRW based method significantly outperformed unsupervised random walk based queries, and models with learned edge weights. Unfortunately, PCRW query systems are expensive to evaluate. In this study we evaluate the use of approximations to the computation of the PCRW distributions, including fingerprinting, particle filtering, and truncation strategies. In experiments on several recommendation and retrieval problems using two large scientific publications corpora we show speedups of factors of 2 to 100 with little loss in accuracy.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 FastQueryExecutionforRetrievalM	William W. Cohen Ni Lao			Fast Query Execution for Retrieval Models based on Path-constrained Random Walks		KDD-2010 Proceedings		10.1145/1835804.1835916		2010