  The lifelong user behavior sequence provides abundant information of user
preference and gains impressive improvement in the recommendation task, however
increases computational consumption significantly. To meet the severe latency
requirement in online service, a short sub-sequence is sampled based on
similarity to the target item. Unfortunately, items not in the sub-sequence are
abandoned, leading to serious information loss.
  In this paper, we propose a new efficient paradigm to model the full lifelong
sequence, which is named as \textbf{I}nteraction \textbf{F}idelity
\textbf{A}ttention (\textbf{IFA}). In IFA, we input all target items in the
candidate set into the model at once, and leverage linear transformer to reduce
the time complexity of the cross attention between the candidate set and the
sequence without any interaction information loss. We also additionally model
the relationship of all target items for optimal set generation, and design
loss function for better consistency of training and inference. We demonstrate
the effectiveness and efficiency of our model by off-line and online
experiments in the recommender system of Kuaishou.
