7,338 research outputs found
Parity-violating coupling constant from the flavor-conserving effective weak chiral Lagrangian
We investigate the parity-violating pion-nucleon-nucleon coupling constant
, based on the chiral quark-soliton model. We employ an effective
weak Hamiltonian that takes into account the next-to-leading order corrections
from QCD to the weak interactions at the quark level. Using the gradient
expansion, we derive the leading-order effective weak chiral Lagrangian with
the low-energy constants determined. The effective weak chiral Lagrangian is
incorporated in the chiral quark-soliton model to calculate the
parity-violating constant . We obtain a value of about
at the leading order. The corrections from the next-to-leading order
reduce the leading order result by about 20~\%.Comment: 12 page
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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