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This paper introduces the Neural Memory Kalman Fusion Recommender (NMKFR), a novel framework designed to address the challenges of item cold-start recommendation in dynamic environments by integrating a Titans-based semantic encoder with time-aware Kalman state tracking. The framework effectively combines static content, early feedback, and temporal-state evidence, leveraging posterior covariance as an uncertainty signal to enhance memory retrieval and adaptively fuse static and temporal information. Experimental results on Amazon Video Games and MovieLens-32M demonstrate that NMKFR outperforms existing methods, achieving robust performance while managing uncertainty effectively.
NMKFR achieves superior cold-start recommendations by intelligently fusing semantic and temporal data, outperforming traditional methods in dynamic environments.
Item cold-start recommendation is difficult when new items have sparse early interactions and appear in recommendation environments that keep changing over time. Static content, early feedback, and temporal-state evidence are all useful, but their reliability varies across the item lifecycle. This work proposes a framework--Neural Memory Kalman Fusion Recommender (NMKFR), which combines a Titans-based semantic encoder with time-aware Kalman state tracking. The semantic branch extracts memory-enhanced item observations from text, while the temporal branch estimates latent states under irregular interaction intervals. The NMKFR further uses posterior covariance as an uncertainty signal to calibrate semantic memory retrieval and adaptive static-temporal fusion. Experiments on Amazon Video Games and MovieLens-32M evaluate NMKFR under time-aware and item cold-start protocols using sampled candidate ranking. Across the reported comparisons, ablations, diagnostics, and robustness analyses, NMKFR achieves the strongest retained results and exhibits bounded uncertainty-related internal behavior. These findings provide empirical evidence for posterior-covariance-guided semantic-temporal fusion under the evaluated offline settings.
