6,866 research outputs found
Ultra accurate collaborative information filtering via directed user similarity
A key challenge of the collaborative filtering (CF) information filtering is
how to obtain the reliable and accurate results with the help of peers'
recommendation. Since the similarities from small-degree users to large-degree
users would be larger than the ones opposite direction, the large-degree users'
selections are recommended extensively by the traditional second-order CF
algorithms. By considering the users' similarity direction and the second-order
correlations to depress the influence of mainstream preferences, we present the
directed second-order CF (HDCF) algorithm specifically to address the challenge
of accuracy and diversity of the CF algorithm. The numerical results for two
benchmark data sets, MovieLens and Netflix, show that the accuracy of the new
algorithm outperforms the state-of-the-art CF algorithms. Comparing with the CF
algorithm based on random-walks proposed in the Ref.7, the average ranking
score could reach 0.0767 and 0.0402, which is enhanced by 27.3\% and 19.1\% for
MovieLens and Netflix respectively. In addition, the diversity, precision and
recall are also enhanced greatly. Without relying on any context-specific
information, tuning the similarity direction of CF algorithms could obtain
accurate and diverse recommendations. This work suggests that the user
similarity direction is an important factor to improve the personalized
recommendation performance.Comment: 6 pages, 4 figure
Locating influential nodes via dynamics-sensitive centrality
With great theoretical and practical significance, locating influential nodes
of complex networks is a promising issues. In this paper, we propose a
dynamics-sensitive (DS) centrality that integrates topological features and
dynamical properties. The DS centrality can be directly applied in locating
influential spreaders. According to the empirical results on four real networks
for both susceptible-infected-recovered (SIR) and susceptible-infected (SI)
spreading models, the DS centrality is much more accurate than degree,
-shell index and eigenvector centrality.Comment: 6 pages, 1 table and 2 figure
Spin-orbit coupling induced Mott transition in CaSrRuO (0<x<0.2)
We propose a new mechanism for the paramagnetic metal-insulator transition in
the layered perovskite CaSrRuO (0<x<0.2). The LDA+U
approach including spin-orbit coupling is used to calculate the electronic
structures. In CaRuO, we show that the spin-orbit effect is
strongly enhanced by the Coulomb repulsion, which leads to an insulating phase.
When Ca is substituted by Sr, the effective spin-orbit splitting is reduced due
to the increasing bandwidth of the degenerate and orbitals.
For x=0.2, the compound is found to be metallic. We show that these results are
in good agreement with the experimental phase diagram.Comment: 4 pages, 4 figure
- …