RecSys 2026 / Recommender Systems

Zero-Observation User Reactivation with Gap-Driven Dimensional Gating

A RecSys 2026 paper defining zero-observation user reactivation and introducing DeltaGate, a frozen-backbone dimensional gating plugin for long-gap returning users.

DOI Publication record Code

Research area

User reactivation, generative recommendation, Semantic-ID interface diagnostics, dynamic retrieval, graph embedding, counterfactual watch-time prediction, and efficient CTR modeling form the main technical line.

Publication record

Authors
Jiandong Ding, Tianying Liu, Fuyuan Liu, Huijie Qin, Tiandeng Wu
Venue
Proceedings of the 20th ACM Conference on Recommender Systems (RecSys '26)
Year
2026
Area
Recommender Systems

Problem

Returning users may have months or years without observed behavior, so a model's last user representation can become unreliable even when pre-gap history exists.

Method

DeltaGate keeps the sequential recommendation backbone frozen and learns a dimension-wise gate that routes each representation dimension between personalized history and a learned global prior.

Findings

Under the Gap-Synthesize Protocol on Amazon datasets, standard sequential recommenders decline as inactivity gaps widen; in the >365d Video Games bucket, DG-BERT4Rec reaches 0.046 Hit@10 versus 0.025 for BERT4Rec.

Deployment boundary

End-to-end retraining can reach higher absolute accuracy, while the frozen DeltaGate plugin preserves zero backbone drift, uses 66K trainable parameters, and keeps the adaptation behavior inspectable.