Improving document ranking based on logs of search engine users

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.