  Historical behaviors have shown great effect and potential in various
prediction tasks, including recommendation and information retrieval. The
overall historical behaviors are various but noisy while search behaviors are
always sparse. Most existing approaches in personalized search ranking adopt
the sparse search behaviors to learn representation with bottleneck, which do
not sufficiently exploit the crucial long-term interest. In fact, there is no
doubt that user long-term interest is various but noisy for instant search, and
how to exploit it well still remains an open problem.
  To tackle this problem, in this work, we propose a novel model named
Query-dominant user Interest Network (QIN), including two cascade units to
filter the raw user behaviors and reweigh the behavior subsequences.
Specifically, we propose a relevance search unit (RSU), which aims to search a
subsequence relevant to the query first and then search the sub-subsequences
relevant to the target item. These items are then fed into an attention unit
called Fused Attention Unit (FAU). It should be able to calculate attention
scores from the ID field and attribute field separately, and then adaptively
fuse the item embedding and content embedding based on the user engagement of
past period. Extensive experiments and ablation studies on real-world datasets
demonstrate the superiority of our model over state-of-the-art methods. The QIN
now has been successfully deployed on Kuaishou search, an online video search
platform, and obtained 7.6% improvement on CTR.
