  Context information in search sessions has proven to be useful for capturing
user search intent. Existing studies explored user behavior sequences in
sessions in different ways to enhance query suggestion or document ranking.
However, a user behavior sequence has often been viewed as a definite and exact
signal reflecting a user's behavior. In reality, it is highly variable: user's
queries for the same intent can vary, and different documents can be clicked.
To learn a more robust representation of the user behavior sequence, we propose
a method based on contrastive learning, which takes into account the possible
variations in user's behavior sequences. Specifically, we propose three data
augmentation strategies to generate similar variants of user behavior sequences
and contrast them with other sequences. In so doing, the model is forced to be
more robust regarding the possible variations. The optimized sequence
representation is incorporated into document ranking. Experiments on two real
query log datasets show that our proposed model outperforms the
state-of-the-art methods significantly, which demonstrates the effectiveness of
our method for context-aware document ranking.
