Search engine logs provide significant information about user preferences. We propose an algorithm that improves search engine ranking quality by using log mining and machine learning. The corresponding evaluation shows a significant improvement in the ranking quality on real-world large-scale datasets. The proposed algorithm allows parallel processing of large-scale data using the MapReduce framework. The developed approach is also applicable to a wide range of log mining tasks. This work is supported by the Russian Foundation for Basic Research (project 12-07-31225).

Keywords: search engines, machine learning, log mining

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