Interest Sequence for User Modeling in Industrial Short-Form Video Recommendation

Abstract

Ultra-long user history modeling has been a highly effective approach in modern industrial recommendation systems, with most works heavily utilizing search-based methods and summarization based methods to map massive user interaction logs into latent user representation with algorithm designs to address the scaling challenge. Despite their success in user interest prediction, previous works are presented with significant scaling challenges due to the increasing computation cost in regards to sequence length and inherent information noise of item-based sequence. Furthermore, the lack of semantic interpretability and explicit interest segmentation often results in the loss of long-tail interest exploration. To address these limitations, we propose a novel Interest Sequence Modeling module and detail the real-world deployment within a production-scale ranking model serving billions of users. We introduce the User Interest Tree Profile (UITP), an explicit representation strategy that complements existing implicit chronological sequences by aggregating lifelong engagement metrics across a hierarchical taxonomy to extract explicit historical interest tokens of users. Through sequential modeling, this streaming asynchronous profile is processed into real-time interest-based sequence for model consumption. Offline evaluations demonstrate statistically significant improvements across all prediction targets and extensive live A/B experiments reveal core engagements gains, as well as highly beneficial ecosystem impacts: a +2.43% increase in exploratory views, a +4.74% boost in global inter-category diversity, and robust gains in fresh content engagement while maintaining low-latency serving constraints without large training overhead.