Deep Interest Network for Click-Through Rate Prediction

To better extract users' interest by exploiting the rich historical behavior data is crucial for building the click-through rate (CTR) prediction model in the online advertising system in e-commerce industry. There are two key observations on user behavior data: i) \textbf{diversity}. Users are interested in different kinds of goods when visiting e-commerce site. ii) \textbf{local activation}. Whether users click or not click a good depends only on part of their related historical behavior. However, most traditional CTR models lack of capturing these structures of behavior data. In this paper, we introduce a new proposed model, Deep Interest Network (DIN), which is developed and deployed in the display advertising system in Alibaba. DIN represents users' diverse interests with an interest distribution and designs an attention-like network structure to locally activate the related interests according to the candidate ad, which is proven to be effective and significantly outperforms traditional model. Overfitting problem is easy to encounter on training such industrial deep network with large scale sparse inputs. We study this problem carefully and propose a useful adaptive regularization technique.