  Click-through rate (CTR) prediction plays a pivotal role in the success of
recommendations. Inspired by the recent thriving of language models (LMs), a
surge of works improve prediction by organizing user behavior data in a
\textbf{textual} format and using LMs to understand user interest at a semantic
level. While promising, these works have to truncate the textual data to reduce
the quadratic computational overhead of self-attention in LMs. However, it has
been studied that long user behavior data can significantly benefit CTR
prediction. In addition, these works typically condense user diverse interests
into a single feature vector, which hinders the expressive capability of the
model. In this paper, we propose a \textbf{T}extual \textbf{B}ehavior-based
\textbf{I}nterest Chunking \textbf{N}etwork (TBIN), which tackles the above
limitations by combining an efficient locality-sensitive hashing algorithm and
a shifted chunk-based self-attention. The resulting user diverse interests are
dynamically activated, producing user interest representation towards the
target item. Finally, the results of both offline and online experiments on
real-world food recommendation platform demonstrate the effectiveness of TBIN.
