Action-On-Item Preference Flow: A Shared Event Schema for Predictive and Generative Personalization

Personalization histories vary across domains because surface actions and objectives differ: users watch or skip movies, click or ignore news, or request recommendation, summarization, question answering, or response generation. Rather than build a separate history encoder for each action space, we rewrite native interactions into a shared *action-on-item event schema*: a specific action on a specific item becomes positive, negative, or command-like evidence over a target-locus embedding whose geometry induces item-kind abstraction. Task requests become generalized commands over the active item-side abstraction. The modeling problem is how this evidence enters a user-specific state, persists, and is read out by the active command. We formulate the Multi-Timescale State Hypothesis (MTSH), where signed and command-like evidence flows through long-term stable interests, recency-sensitive short-term interests, and bursty episodic traces. We instantiate MTSH with $\texttt{PerTIDE}$, an action-conditioned multi-timescale encoder that learns a user-specific preference-history state and uses command-conditioned readout to produce a task-ready state. We evaluate $\texttt{PerTIDE}$ on MovieLens, MIND, and PENS for prediction, and PENS/OpenAI-Reddit for generation. $\texttt{PerTIDE}$ improves over the strongest baselines without explicit trace factorization by +2.36/+3.47, +10.05/+9.01, and +0.90/+1.42 MRR/nDCG on MovieLens, MIND, and PENS; and over the strongest two-shot large language model by +0.10/+0.18 and +0.04/+0.19 PerSEval-JSD/PerSEval-METEOR on PENS and OpenAI-Reddit. Removing action conditioning and command-conditioned readout degrades MIND by 6.52 MRR / 9.13 HR@10 and 4.14 MRR / 4.42 HR@10. Trace diagnostics show L/S/E specialization, while the full $L+S+E$ model is strongest under mixed stable, recency-sensitive, and episodic command evidence. These results support action-conditioned multi-timescale preference flow as a reusable user-history encoding principle.