An Optimization Framework for Weighting Implicit Relevance Labels for Personalized Web Search
Yury Ustinovskiy, Gleb Gusev, Pavel Serdyukov · 2015
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 relevance labels leads to a suboptimal personalized ranking.