2009 BBMBayesianBrowsingModelfromPet

• (Liu et al., 2009) ⇒ Chao Liu, Fan Guo, and Christos Faloutsos. (2009). “BBM: Bayesian Browsing Model from Petabyte-scale Data.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557081

Subject Headings:

Notes

• Categories and Subject Descriptors: H.3.3 Information Storage and Retrieval: Information Search and Retrieval - Retrieval Models
• General Terms: Algorithms, Experimentation, Performance

Cited By

• http://scholar.google.com/scholar?q=%22BBM%3A+bayesian+browsing+model+from+petabyte-scale+data%22+2009
• http://portal.acm.org/citation.cfm?doid=1557019.1557081&preflayout=flat#citedby

Quotes

Author Keywords

Bayesian Models, Click log Analysis, Web Search

Abstract

Given a quarter of petabyte click log data, how can we estimate the relevance of each URL for a given query? In this paper, we propose the Bayesian Browsing Model (BBM), a new modeling technique with following advantages : (a) it does exact inference; (b) it is single-pass and parallelizable; (c) it is effective. We present two sets of experiments to test model effectiveness and efficiency. On the first set of over 50 million search instances of 1.1 million distinct queries, BBM outperforms the state-of-the-art competitor by 29.2% in log-likelihood while being 57 times faster. On the second click-log set, spanning a quarter of petabyte data, we showcase the scalability of BBM : we implemented it on a commercial MapReduce cluster, and it took only 3 hours to compute the relevance for 1.15 billion distinct query-URL pairs.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 BBMBayesianBrowsingModelfromPet	Christos Faloutsos Chao Liu Fan Guo			BBM: Bayesian Browsing Model from Petabyte-scale Data		KDD-2009 Proceedings		10.1145/1557019.1557081		2009