We present our solution to the Yandex Personalized Web Search Challenge. The
aim of this challenge was to use the historical search logs to personalize
top-N document rankings for a set of test users. We used over 100 features
extracted from user- and query-depended contexts to train neural net and
tree-based learning-to-rank and regression models. Our final submission, which
was a blend of several different models, achieved an NDCG@10 of 0.80476 and
placed 4'th amongst the 194 teams winning 3'rd prize