document: BBM : bayesian browsing model from petabyte-scale data 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 out-performs 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 .
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