Extracting users' interests from their lifelong behavior sequence is crucial
for predicting Click-Through Rate (CTR). Most current methods employ a
two-stage process for efficiency: they first select historical behaviors
related to the candidate item and then deduce the user's interest from this
narrowed-down behavior sub-sequence. This two-stage paradigm, though effective,
leads to information loss. Solely using users' lifelong click behaviors doesn't
provide a complete picture of their interests, leading to suboptimal
performance. In our research, we introduce the Deep Group Interest Network
(DGIN), an end-to-end method to model the user's entire behavior history. This
includes all post-registration actions, such as clicks, cart additions,
purchases, and more, providing a nuanced user understanding. We start by
grouping the full range of behaviors using a relevant key (like item_id) to
enhance efficiency. This process reduces the behavior length significantly,
from O(10^4) to O(10^2). To mitigate the potential loss of information due to
grouping, we incorporate two categories of group attributes. Within each group,
we calculate statistical information on various heterogeneous behaviors (like
behavior counts) and employ self-attention mechanisms to highlight unique
behavior characteristics (like behavior type). Based on this reorganized
behavior data, the user's interests are derived using the Transformer
technique. Additionally, we identify a subset of behaviors that share the same
item_id with the candidate item from the lifelong behavior sequence. The
insights from this subset reveal the user's decision-making process related to
the candidate item, improving prediction accuracy. Our comprehensive
evaluation, both on industrial and public datasets, validates DGIN's efficacy
and efficiency