  Contextual information in search sessions is important for capturing users'
search intents. Various approaches have been proposed to model user behavior
sequences to improve document ranking in a session. Typically, training samples
of (search context, document) pairs are sampled randomly in each training
epoch. In reality, the difficulty to understand user's search intent and to
judge document's relevance varies greatly from one search context to another.
Mixing up training samples of different difficulties may confuse the model's
optimization process. In this work, we propose a curriculum learning framework
for context-aware document ranking, in which the ranking model learns matching
signals between the search context and the candidate document in an
easy-to-hard manner. In so doing, we aim to guide the model gradually toward a
global optimum. To leverage both positive and negative examples, two curricula
are designed. Experiments on two real query log datasets show that our proposed
framework can improve the performance of several existing methods
significantly, demonstrating the effectiveness of curriculum learning for
context-aware document ranking.
