  Click-Through Rate (CTR) prediction, crucial in applications like recommender
systems and online advertising, involves ranking items based on the likelihood
of user clicks. User behavior sequence modeling has marked progress in CTR
prediction, which extracts users' latent interests from their historical
behavior sequences to facilitate accurate CTR prediction. Recent research
explores using implicit feedback sequences, like unclicked records, to extract
diverse user interests. However, these methods encounter key challenges: 1)
temporal misalignment due to disparate sequence time ranges and 2) the lack of
fine-grained interaction among feedback sequences. To address these challenges,
we propose a novel framework called TEM4CTR, which ensures temporal alignment
among sequences while leveraging auxiliary feedback information to enhance
click behavior at the item level through a representation projection mechanism.
Moreover, this projection-based information transfer module can effectively
alleviate the negative impact of irrelevant or even potentially detrimental
components of the auxiliary feedback information on the learning process of
click behavior. Comprehensive experiments on public and industrial datasets
confirm the superiority and effectiveness of TEM4CTR, showcasing the
significance of temporal alignment in multi-feedback modeling.
