ChronoID: Infusing Explicit Temporal Signals into Semantic IDs for Generative Recommendation
This work addresses a fundamental limitation in generative recommendation for practitioners, offering a principled way to incorporate temporal signals into semantic IDs.
The paper identifies that semantic IDs in generative recommendation lack explicit temporal information, conflating interactions from different times. It proposes ChronoID, a framework for time-aware semantic ID learning, and shows through a new benchmark that explicit temporal infusion improves recommendation performance.
Semantic IDs are crucial in generative recommendation, but with a fundamental limitation: temporal information is not well incorporated into semantic IDs. Instead, time influences recommendation only implicitly (e.g., through session construction heuristics, preference alignment, or sequence order), while existing semantic ID learning remains entirely time-agnostic. This design conflates interactions occurring under distinct temporal contexts into identical semantic representations, implicitly assuming that item semantics and user intent are temporally stationary. Such an assumption is misaligned with real-world recommendation scenarios, where evolving interaction rhythms play a central role. In this work, we investigate where and how the explicit time should be incorporated into semantic ID for generative recommendation. First, we systematically characterize the design space along three orthogonal dimensions of temporal signals and present a unified framework, ChronoID, for time-aware semantic ID learning. Then, by contributing a new time-explicit generation recommendation benchmark, ChronoID answers the questions: what is the effective way of infusing time, how to design the architecture, and where does the gain come from.