Improving Quality of CTR Prediction Through Multi-task Learning

Student: Kokhtev Vadim

Supervisor: Daniil Yashkov

Educational Programme: Financial Technology and Data Analysis (Master)

Year of Graduation: 2020

For real estate classifieds, the task of ranking content is extremely relevant. This paper considers approaches of multi-task learning in ranking models for click-through rate prediction. In paper proposed and tested training options for multi-task neural network models that were not previously found in scientific papers, as well as shown their effectiveness on real data from the Yandex.Realty service.

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