  Capturing the evolving trends of user interest is important for both
recommendation systems and advertising systems, and user behavior sequences
have been successfully used in Click-Through-Rate(CTR) prediction problems.
However, if the user interest is learned on the basis of item-level behaviors,
the performance may be affected by the following two issues. Firstly, some
casual outliers might be included in the behavior sequences as user behaviors
are likely to be diverse. Secondly, the span of time intervals between user
behaviors is random and irregular, for which a RNN-based module employed from
NLP is not perfectly adaptive. To handle these two issues, we propose the
Knowledge aware Adaptive Session multi-Topic network(KAST). It can adaptively
segment user sessions from the whole user behavior sequence, and maintain
similar intents in the same session. Furthermore, in order to improve the
quality of session segmentation and representation, a knowledge-aware module is
introduced so that the structural information from the user-item interaction
can be extracted in an end-to-end manner, and a marginal based loss with these
information is merged into the major loss. Through extensive experiments on
public benchmarks, we demonstrate that KAST can achieve superior performance
than state-of-the-art methods for CTR prediction, and key modules and
hyper-parameters are also evaluated.
