Results 51 to 60 of about 142,624 (305)
Predicting Emerging Trends on Social Media by Modeling it as Temporal Bipartite Networks
IEEE Access, 2020 The behavior of peoples' request for a post on online social media is a stochastic process that makes post's ranking highly skewed in nature. We mean peoples interest for a post can grow/decay exponentially or linearly.
Asif Khan +6 more
doaj +1 more source
Your relevance feedback is essential: enhancing the learning to rank using the virtual feature based logistic regression. [PDF]
PLoS ONE, 2012 Information retrieval applications have to publish their output in the form of ranked lists. Such a requirement motivates researchers to develop methods that can automatically learn effective ranking models. Many existing methods usually perform analysis
Fei Cai +3 more
doaj +1 more source
Using the quantum probability ranking principle to rank interdependent documents [PDF]
, 2010 A known limitation of the Probability Ranking Principle (PRP) is that it does not cater for dependence between documents. Recently, the Quantum Probability Ranking Principle (QPRP) has been proposed, which implicitly captures dependencies between ...
A. Khrennikov +13 more
core +2 more sources
IEEE Access, 2021 The rapid growth in the number of scholarly documents on the Web and in other digital platforms makes it challenging for researchers to find research publications most relevant to their information needs.
Shah Khalid +4 more
doaj +1 more source
Combination of content analysis and context features for digital photograph retrieval. [PDF]
, 2005 In recent years digital cameras have seen an enormous rise in popularity, leading to a huge increase in the quantity of digital photos being taken. This brings with it the challenge of organising these large collections.
Gurrin, Cathal +3 more
core +1 more source
Automatic Ranking of Information Retrieval Systems
Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 2018 Typical information retrieval system evaluation requires expensive manually-collected relevance judgments of documents, which are used to rank retrieval systems. Due to the high cost associated with collecting relevance judgments and the ever-growing scale of data to be searched in practice, ranking of retrieval systems using manual judgments is ...
openaire +2 more sources
Ranking structured documents using utility theory in the Bayesian network retrieval model [PDF]
, 2003 In this paper a new method based on Utility and Decision theory is presented to deal with structured documents. The aim of the application of these methodologies is to refine a first ranking of structural units, generated by means of an Information ...
F. Crestani +10 more
core +3 more sources
Frontiers in Big Data, 2022 Here we study the semantic search and retrieval problem in biomedical digital libraries. First, we introduce MedGraph, a knowledge graph embedding-based method that provides semantic relevance retrieval and ranking for the biomedical literature indexed ...
Islam Akef Ebeid
doaj +1 more source
Cross-modal Deep Metric Learning with Multi-task Regularization
, 2017 DNN-based cross-modal retrieval has become a research hotspot, by which users can search results across various modalities like image and text. However, existing methods mainly focus on the pairwise correlation and reconstruction error of labeled data ...
Huang, Xin, Peng, Yuxin
core +1 more source
Molecular Oncology, EarlyView.Chronic TGF‐β exposure drives epithelial HCC cells from a senescent state to a TGF‐β resistant mesenchymal phenotype. This transition is characterized by the loss of Smad3‐mediated signaling, escape from senescence, enhanced invasiveness and metastatic potential, and upregulation of key resistance modulators such as MARK1 and GRM8, ultimately promoting
Minenur Kalyoncu +11 more
wiley +1 more source