Antiferromagnetic and structural transitions in the superoxide KO2 from first principles: A 2p-electron system with spin-orbital-lattice coupling

KO2 exhibits concomitant antiferromagnetic (AFM) and structural transitions, both of which originate from the open-shell 2p electrons of O$_{2}^{-}$ molecules. The structural transition is accompanied by the coherent tilting of O$_{2}^{-}$ molecular axes. The interplay among the spin-orbital-lattice degrees of freedom in KO2 is investigated by employing the first-principles electronic structure theory and the kinetic-exchange interaction scheme. We have shown that the insulating nature of the high symmetry phase of KO2 at high temperature (T) arises from the combined effect of the spin-orbit coupling and the strong Coulomb correlation of O 2p electrons. In contrast, for the low symmetry phase of KO2 at low T with the tilted O$_{2}^{-}$ molecular axes, the band gap and the orbital ordering are driven by the combined effects of the crystal-field and the strong Coulomb correlation. We have verified that the emergence of the O 2p ferro-orbital ordering is essential to achieve the observed AFM structure for KO2

Click-aware purchase prediction with push at the top

Eliciting user preferences from purchase records for performing purchase prediction is challenging because negative feedback is not explicitly observed, and because treating all non-purchased items equally as negative feedback is unrealistic. Therefore, in this study, we present a framework that leverages the past click records of users to compensate for the missing user-item interactions of purchase records, i.e., non-purchased items. We begin by formulating various model assumptions, each one assuming a different order of user preferences among purchased, clicked-but-not-purchased, and non-clicked items, to study the usefulness of leveraging click records. We implement the model assumptions using the Bayesian personalized ranking model, which maximizes the area under the curve for bipartite ranking. However, we argue that using click records for bipartite ranking needs a meticulously designed model because of the relative unreliableness of click records compared with that of purchase records. Therefore, we ultimately propose a novel learning-to-rank method, called P3Stop, for performing purchase prediction. The proposed model is customized to be robust to relatively unreliable click records by particularly focusing on the accuracy of top-ranked items. Experimental results on two real-world e-commerce datasets demonstrate that P3STop considerably outperforms the state-of-the-art implicit-feedback-based recommendation methods, especially for top-ranked items.Comment: For the final published journal version, see https://doi.org/10.1016/j.ins.2020.02.06