BBM: Bayesian Browsing Model from Petabyte-scale Data
| Citations: | 5 - 2 self |
BibTeX
@MISC{Liu_bbm:bayesian,
author = {Chao Liu and Fan Guo and Christos Faloutsos},
title = {BBM: Bayesian Browsing Model from Petabyte-scale Data},
year = {}
}
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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 loglikelihood while being 57 times faster. On the second clicklog 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.
