4,385 research outputs found
iTrace: An Implicit Trust Inference Method for Trust-aware Collaborative Filtering
The growth of Internet commerce has stimulated the use of collaborative
filtering (CF) algorithms as recommender systems. A collaborative filtering
(CF) algorithm recommends items of interest to the target user by leveraging
the votes given by other similar users. In a standard CF framework, it is
assumed that the credibility of every voting user is exactly the same with
respect to the target user. This assumption is not satisfied and thus may lead
to misleading recommendations in many practical applications. A natural
countermeasure is to design a trust-aware CF (TaCF) algorithm, which can take
account of the difference in the credibilities of the voting users when
performing CF. To this end, this paper presents a trust inference approach,
which can predict the implicit trust of the target user on every voting user
from a sparse explicit trust matrix. Then an improved CF algorithm termed
iTrace is proposed, which takes advantage of both the explicit and the
predicted implicit trust to provide recommendations with the CF framework. An
empirical evaluation on a public dataset demonstrates that the proposed
algorithm provides a significant improvement in recommendation quality in terms
of mean absolute error (MAE).Comment: 6 pages, 4 figures, 1 tabl
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microRNA-33a-5p increases radiosensitivity by inhibiting glycolysis in melanoma.
Glycolysis was reported to have a positive correlation with radioresistance. Our previous study found that the miR-33a functioned as a tumor suppressor in malignant melanoma by targeting hypoxia-inducible factor1-alpha (HIF-1α), a gene known to promote glycolysis. However, the role of miR-33a-5p in radiosensitivity remains to be elucidated. We found that miR-33a-5p was downregulated in melanoma tissues and cells. Cell proliferation was downregulated after overexpression of miR-33a-5p in WM451 cells, accompanied by a decreased level of glycolysis. In contrast, cell proliferation was upregulated after inhibition of miR-33a-5p in WM35 cells, accompanied by increased glycolysis. Overexpression of miR-33a-5p enhanced the sensitivity of melanoma cells to X-radiation by MTT assay, while downregulation of miR-33a-5p had the opposite effects. Finally, in vivo experiments with xenografts in nude mice confirmed that high expression of miR-33a-5p in tumor cells increased radiosensitivity via inhibiting glycolysis. In conclusions, miR-33a-5p promotes radiosensitivity by negatively regulating glycolysis in melanoma
Balancing Augmentation with Edge-Utility Filter for Signed GNNs
Signed graph neural networks (SGNNs) has recently drawn more attention as
many real-world networks are signed networks containing two types of edges:
positive and negative. The existence of negative edges affects the SGNN
robustness on two aspects. One is the semantic imbalance as the negative edges
are usually hard to obtain though they can provide potentially useful
information. The other is the structural unbalance, e.g. unbalanced triangles,
an indication of incompatible relationship among nodes. In this paper, we
propose a balancing augmentation method to address the above two aspects for
SGNNs. Firstly, the utility of each negative edge is measured by calculating
its occurrence in unbalanced structures. Secondly, the original signed graph is
selectively augmented with the use of (1) an edge perturbation regulator to
balance the number of positive and negative edges and to determine the ratio of
perturbed edges to original edges and (2) an edge utility filter to remove the
negative edges with low utility to make the graph structure more balanced.
Finally, a SGNN is trained on the augmented graph which effectively explores
the credible relationships. A detailed theoretical analysis is also conducted
to prove the effectiveness of each module. Experiments on five real-world
datasets in link prediction demonstrate that our method has the advantages of
effectiveness and generalization and can significantly improve the performance
of SGNN backbones.Comment: 16 page
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