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User preferences change over time and capturing such changes is essential for developing accurate recommender systems. Despite its importance, only a few works in collaborative filtering have addressed this issue. In this paper, we consider evolving preferences and we model user dynamics by introducing and learning a transition matrix for each user's latent vectors between consecutive time windows. Intuitively, the transition matrix for a user summarizes the time-invariant pattern of the evolution for the user. We first extend the conventional probabilistic matrix factorization and then improve upon this solution through its fully Bayesian model. These solutions take advantage of the model complexity and scalability of conventional Bayesian matrix factorization, yet adapt dynamically to user's evolving preferences. We evaluate the effectiveness of these solutions through empirical studies on six large-scale real life data sets.

Stimulation of the subiculum/CA1 of the hippocampal formation evokes monosynaptic field potentials in the prefrontal cortex (PFC). High-frequency stimulation of the hippocampus (HPC) can induce long-term potentiation (LTP) in this hippocampo-prefrontal cortical (hippo-PFC) pathway. Previous studies have shown that dopamine and serotonin modulate hippo-PFC LTP. Here, we investigated whether the locus coeruleus (LC) and noradrenaline (NA) can modulate LTP in the rat hippo-PFC pathway. Stimulation of the LC in combination with stimulation of the HPC increased hippo-PFC LTP. Infusion of lidocaine into the LC reduced hippo-PFC LTP. Administration of the noradrenaline reuptake inhibitor, nisoxetine or the alpha2 adrenoceptor antagonist, idazoxan prior to high-frequency stimulation of the HPC enhanced hippo-LTP. In contrast, administration of clonidine, an alpha2 adrenoceptor agonist, impaired hippo-PFC LTP. Partial noradrenergic (NAergic) lesioning with DSP-4 also impaired hippo-PFC LTP. In conclusion, the LC and NAergic mechanisms modulate hippo-PFC LTP.