6,194 research outputs found
Disposition Effect on Two Classical Expected Utility Models:\ud Exponential and Power
A disposition effect is the observation that investors tend to sell winning stocks too early and hold losing stocks too long. In this paper, we investigate whether expected utility theory explains the disposition effect. We implement two models of expected utility theory: exponential and power. We show that for reasonable parameter values the disposition effect can be explained by expected utility theory
A note on the almost one half holomorphic pinching
Motivated by a previous work of Zheng and the second named author, we study
pinching constants of compact K\"ahler manifolds with positive holomorphic
sectional curvature. In particular we prove a gap theorem following the work of
Petersen and Tao on Riemannian manifolds with almost quarter-pinched sectional
curvature.Comment: 6 pages. This is the version which the authors submitted to a journal
for consideration for publication in June 2017. The reference has not been
updated since the
Identifying vital edges in Chinese air route network via memetic algorithm
Due to its rapid development in the past decade, air transportation system
has attracted considerable research attention from diverse communities. While
most of the previous studies focused on airline networks, here we
systematically explore the robustness of the Chinese air route network, and
identify the vital edges which form the backbone of Chinese air transportation
system. Specifically, we employ a memetic algorithm to minimize the network
robustness after removing certain edges hence the solution of this model is the
set of vital edges. Counterintuitively, our results show that the most vital
edges are not necessarily the edges of highest topological importance, for
which we provide an extensive explanation from the microscope of view. Our
findings also offer new insights to understanding and optimizing other
real-world network systems
Neural Collaborative Ranking
Recommender systems are aimed at generating a personalized ranked list of
items that an end user might be interested in. With the unprecedented success
of deep learning in computer vision and speech recognition, recently it has
been a hot topic to bridge the gap between recommender systems and deep neural
network. And deep learning methods have been shown to achieve state-of-the-art
on many recommendation tasks. For example, a recent model, NeuMF, first
projects users and items into some shared low-dimensional latent feature space,
and then employs neural nets to model the interaction between the user and item
latent features to obtain state-of-the-art performance on the recommendation
tasks. NeuMF assumes that the non-interacted items are inherent negative and
uses negative sampling to relax this assumption. In this paper, we examine an
alternative approach which does not assume that the non-interacted items are
necessarily negative, just that they are less preferred than interacted items.
Specifically, we develop a new classification strategy based on the widely used
pairwise ranking assumption. We combine our classification strategy with the
recently proposed neural collaborative filtering framework, and propose a
general collaborative ranking framework called Neural Network based
Collaborative Ranking (NCR). We resort to a neural network architecture to
model a user's pairwise preference between items, with the belief that neural
network will effectively capture the latent structure of latent factors. The
experimental results on two real-world datasets show the superior performance
of our models in comparison with several state-of-the-art approaches.Comment: Proceedings of the 2018 ACM on Conference on Information and
Knowledge Managemen
- …