The field of long behavior sequence modeling and recommendation systems is moving towards more innovative and effective approaches to capture users' long-term preferences and behaviors. Researchers are exploring new methods to address the challenges of sparse user behavior data, feature taxonomy gaps, and inter-field interference. Notably, the development of unified frameworks and models that can handle complex feature hierarchies, mitigate inter-field conflicts, and capture high-order behavioral correlations is gaining traction. These advancements have the potential to significantly improve the performance of recommendation systems and enhance user experience.

Some noteworthy papers in this area include: The paper on VIrtual Sequential Target Attention (VISTA) which proposes a novel two-stage modeling framework that enables scalability to lifelong user histories while keeping downstream training and inference costs fixed. The paper on Bid2X which introduces a bidding foundation model that learns a fundamental function from data in various scenarios, achieving significant improvements in offline and online metrics. The paper on OneTrans which proposes a unified Transformer backbone that simultaneously performs user-behavior sequence modeling and feature interaction, yielding a 5.68% lift in per-user GMV in online A/B tests. The paper on Barlow Twins for Sequential Recommendation (BT-SR) which introduces a novel non-contrastive SSL framework that integrates the Barlow Twins redundancy-reduction principle into a Transformer-based next-item recommender, consistently improving next-item prediction accuracy and significantly enhancing long-tail item coverage and recommendation calibration.