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Implicit feedback from users of a web search engine is an essential source providing consistent personal relevance labels from the actual population of users. However, previous studies on personalized search employ this source in a rather straightforward manner. Basically, documents that were clicked on get maximal gain, and the rest of the documents are assigned the zero gain. As we demonstrate in our paper, a ranking algorithm trained using these gains directly as the ground truth relevancedoi:10.1145/2736277.2741105 dblp:conf/www/UstinovskiyGS15 fatcat:3lzngiwzyzhhheidg4wouoskji