CRAMER: Control via Request-Aware Masking for Editing Recommenders

Zhiyuan Su, Naihe Feng, Luther Qin, Ga Wu

Keywords: Controllable Model Editing, Request-Aware Masking, Parameter-Efficient Adaptation, Sequential Recommendation

TL;DR: We propose CRAMER, a request-aware masking framework that adapts frozen sequential recommenders to natural-language requests in real time, achieving controllability and efficiency without retraining.

Abstract: Sequential recommendation models, while powerful, have limited flexibility in responding to immediate user requests, making it difficult to adapt their recommendations to the user's timely interests. Unfortunately, existing user request adaptation methods often incur high computational overhead due to either 1) retraining the entire backbone network or 2) leveraging the inference ability of large language models (a.k.a. prompt engineering), limiting their applicability in large-scale recommendation services. This paper presents **C**ontrol via **R**equest-**A**ware **M**asking for **E**diting **R**ecommenders (**CRAMER**), a framework that takes users' natural-language requests to immediately change sequential recommendation models' behavior. Specifically, inspired by the model control theory, CRAMER treats user requests as control signals to modulate frozen backbone parameters through masking, achieving instant adaptation to diverse requests while avoiding costly retraining. Experiments on multiple large-scale benchmark datasets show that CRAMER outperforms four state-of-the-art request-aware baselines across multiple recommendation metrics while achieving minimal overhead. Moreover, the proposed framework exhibits enhanced controllability and cross-domain adaptability, establishing a new paradigm for request-aware sequential recommendation.

Primary Area: other topics in machine learning (i.e., none of the above)

Submission Number: 9747