Fast-Weight Memory for Adaptive Recommendation

Modern sequential recommenders repeatedly process interaction histories to capture evolving user preferences, coupling preference adaptation with sequential history modeling. We propose FWPRec, a recommendation model that separates user representation into a stable preference anchor and a fast associative memory for recent behavioral changes. The anchor captures persistent preference information, while fast-weight matrices incorporate new positive and negative feedback through learned gradient-free updates at inference time without modifying global model parameters. This separation allows item representations and long-term user states to be reused across requests, while preference changes are directly incorporated into a fixed-size user-specific state. We evaluate FWPRec on six real-world interaction datasets against static, lightweight adaptation, recurrent, attention-based, and state-space recommenders under controlled ranking settings. FWPRec achieves competitive ranking performance while providing effective adaptation to preference changes, with particularly strong gains when informative positive and negative feedback is available. Further analysis shows that learned memory construction and the separation of stable preferences from fast behavioral corrections are important for effective adaptation. The results demonstrate that fast-weight memory provides an alternative and complementary mechanism to sequential history modeling for capturing dynamic user preferences.