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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
molecules. The structural transition is accompanied by the coherent tilting of
O 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 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
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