ChronoID: Infusing Explicit Temporal Signals into Semantic IDs for Generative Recommendation

2026-06-12

AI Summary

Existing generative recommendation methods employ semantic IDs that do not explicitly model temporal information, limiting their ability to capture the dynamic evolution of user intent and item semantics. To address this limitation, this work proposes ChronoID—a unified time-aware semantic ID learning framework—that systematically characterizes the design space of temporal signals in generative recommendation and investigates their optimal injection points and fusion strategies. The study establishes the first time-explicit benchmark for generative recommendation, incorporating multidimensional temporal signal fusion mechanisms and dedicated datasets. Experimental results demonstrate that explicit temporal modeling significantly outperforms time-agnostic baselines, validating the efficacy of the proposed approach and revealing key factors driving performance gains.

Abstract

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.

Problem

• generative recommendation
• semantic IDs
• temporal signals
• time-aware representation
• user intent dynamics

Innovation

• ChronoID
• generative recommendation
• semantic IDs
• temporal signals
• time-aware representation