  Position bias is a critical problem in information retrieval when dealing
with implicit yet biased user feedback data. Unbiased ranking methods typically
rely on causality models and debias the user feedback through inverse
propensity weighting. While practical, these methods still suffer from two
major problems. First, when inferring a user click, the impact of the
contextual information, such as documents that have been examined, is often
ignored. Second, only the position bias is considered but other issues resulted
from user browsing behaviors are overlooked. In this paper, we propose an
end-to-end Deep Recurrent Survival Ranking (DRSR), a unified framework to
jointly model user's various behaviors, to (i) consider the rich contextual
information in the ranking list; and (ii) address the hidden issues underlying
user behaviors, i.e., to mine observe pattern in queries without any click
(non-click queries), and to model tracking logs which cannot truly reflect the
user browsing intents (untrusted observation). Specifically, we adopt a
recurrent neural network to model the contextual information and estimates the
conditional likelihood of user feedback at each position. We then incorporate
survival analysis techniques with the probability chain rule to mathematically
recover the unbiased joint probability of one user's various behaviors. DRSR
can be easily incorporated with both point-wise and pair-wise learning
objectives. The extensive experiments over two large-scale industrial datasets
demonstrate the significant performance gains of our model comparing with the
state-of-the-arts.

Information Retrieval