LREA: Low-Rank Efficient Attention on Modeling Long-Term User Behaviors for CTR Prediction

• URL: http://arxiv.org/abs/2503.02542v4
• Date: Thu, 08 May 2025 13:24:32 GMT
• Title: LREA: Low-Rank Efficient Attention on Modeling Long-Term User Behaviors for CTR Prediction
• Authors: Xin Song, Xiaochen Li, Jinxin Hu, Hong Wen, Zulong Chen, Yu Zhang, Xiaoyi Zeng, Jing Zhang,
• Abstract summary: We introduce LREA, a novel attention mechanism that overcomes the limitations of existing approaches.<n>LREA incorporates a specially designed loss function to maintain attention capabilities while preserving information integrity.
• Score: 22.366063727224173
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: With the rapid growth of user historical behavior data, user interest modeling has become a prominent aspect in Click-Through Rate (CTR) prediction, focusing on learning user intent representations. However, this complexity poses computational challenges, requiring a balance between model performance and acceptable response times for online services. Traditional methods often utilize filtering techniques. These techniques can lead to the loss of significant information by prioritizing top K items based on item attributes or employing low-precision attention mechanisms. In this study, we introduce LREA, a novel attention mechanism that overcomes the limitations of existing approaches while ensuring computational efficiency. LREA leverages low-rank matrix decomposition to optimize runtime performance and incorporates a specially designed loss function to maintain attention capabilities while preserving information integrity. During the inference phase, matrix absorption and pre-storage strategies are employed to effectively meet runtime constraints. The results of extensive offline and online experiments demonstrate that our method outperforms state-of-the-art approaches.

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