TBIN: Modeling Long Textual Behavior Data for CTR Prediction

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 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 Textual Behavior-based Interest Chunking Network (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.