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This paper introduces CRAMER, a novel framework that utilizes request-aware masking to enable immediate adaptation of sequential recommendation models to user requests without the need for retraining. By treating user requests as control signals, CRAMER modulates frozen backbone parameters, allowing for efficient and flexible recommendation adjustments. Experimental results demonstrate that CRAMER significantly outperforms existing request-aware methods on large-scale benchmarks while maintaining low computational overhead.
Instantaneously adapting recommendation models to user requests without retraining could revolutionize how we personalize user experiences in real-time.
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 Control via Request-Aware Masking for Editing Recommenders (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.
