UxSID: Semantic-Aware User Interests Modeling for Ultra-Long Sequence

Modeling ultra-long user behavior sequences is an essential task for capturing evolving preferences in modern recommender systems, and this research direction has contributed solid gains in the past several years. However, recommendation systems always serve enormous traffic, and extending user sequences sharply increases computational cost, which creates a difficult trade-off between efficiency and effectiveness. To scale to longer user sequences while keeping lightweight serving computation, existing works can be divided into two paradigms: (1) $\textit{Search-based Top-$K$ selection}$, which constructs an $\textbf{item-specific}$ subsequence for each candidate item to avoid facing the ultra-long sequence directly; and (2) $\textit{pre-trained user-interest compression}$, which maps an ultra-long user sequence into a small group of $\textbf{item-agnostic}$ user interest memories so that the online model can perceive user long-term interests via this highly compressed dense memory. Besides the two technical routes (totally item-specific or item-agnostic), we argue that there exists an intermediate path not well explored: preserving partial relevance between the user sequence and the target item, while exposing only limited signals to guide the direction of interest compression. This design does not pursue item-specific user interest compression, but seeks semantic-group shared general user interest memory according to item attributes, where semantically similar items share the same compressed user interest memory. Motivated by this, we propose $\textbf{UxSID}$, a novel framework that bridges this gap by facilitating a target item semantic-aware interaction between $\textbf{U}$ser histories and candidate $\textbf{S}$emantic $\textbf{ID}$s (SIDs). Specifically, UxSID employs a dual-level attention strategy: it first extracts item-agnostic user interests from raw sequences, and then performs a semantic-specific query over global behaviors and those agnostic interests to generate semantic-specific preferences. By adopting this end-to-end architecture, UxSID generates offline embeddings that balance computational parsimony with target items' semantic awareness, while strictly preserving parity with online inference in constant time. Extensive public benchmarks and large-scale A/B tests demonstrate that UxSID achieves state-of-the-art performance, driving a 0.337\% revenue lift in advertising. Open source link: $\url{https://anonymous.4open.science/r/UxSID/}$