scholarly journals Interdisciplinary Collaborator Recommendation Based on Research Content Similarity

2017 ◽  
Vol E100.D (4) ◽  
pp. 785-792 ◽  
Author(s):  
Masataka ARAKI ◽  
Marie KATSURAI ◽  
Ikki OHMUKAI ◽  
Hideaki TAKEDA
Keyword(s):  
Content Similarity

Convergent and Predictive Validity of Implicit and Explicit Anxiety Measures as a Function of Specificity Similarity and Content Similarity

2008 ◽  
Vol 24 (4) ◽  
pp. 254-262 ◽  
Author(s):  
Tobias Gschwendner ◽  
Wilhelm Hofmann ◽  
Manfred Schmitt
Keyword(s):  
Implicit Association Test ◽  
Theoretical Models ◽  
Implicit Measures ◽  
Speech Anxiety ◽  
Behavioral Indicators ◽  
General Anxiety ◽  
Anxious Behavior ◽  
Content Similarity ◽  
Implicit And Explicit ◽  
Validation Strategy

In the present study we applied a validation strategy for implicit measures like the IAT, which complements multitrait-multimethod (MTMM) analyses. As the measurement method (implicit vs. explicit) and underlying representation format (associative vs. propositional) are often confounded, the validation of implicit measures has to go beyond MTMM analysis and requires substantive theoretical models. In the present study (N = 133), we employed such a model ( Hofmann, Gschwendner, Nosek, & Schmitt, 2005 ) and investigated two moderator constructs in the realm of anxiety: specificity similarity and content similarity. In the first session, different general and specific anxiety measures were administered, among them an Implicit Association Test (IAT) general anxiety, an IAT-spider anxiety, and an IAT that assesses speech anxiety. In the second session, participants had to deliver a speech and behavioral indicators of speech anxiety were measured. Results showed that (a) implicit and explicit anxiety measures correlated significantly only on the same specification level and if they measured the same content, and (b) specific anxiety measures best predicted concrete anxious behavior. These results are discussed regarding the validation of implicit measures.

scholarly journals Recommending Mobile Microblog Users via a Tensor Factorization Based on User Cluster Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Xiangwen Liao ◽  
Lingying Zhang ◽  
Jingjing Wei ◽  
Dingda Yang ◽  
Guolong Chen
Keyword(s):  
Network Centrality ◽  
Network Clustering ◽  
Tensor Factorization ◽  
Latent Factors ◽  
Cp Decomposition ◽  
Temporal Features ◽  
User Influence ◽  
Proposed Model ◽  
The Mean ◽  
Content Similarity

User influence is a very important factor for microblog user recommendation in mobile social network. However, most existing user influence analysis works ignore user’s temporal features and fail to filter the marketing users with low influence, which limits the performance of recommendation methods. In this paper, a Tensor Factorization based User Cluster (TFUC) model is proposed. We firstly identify latent influential users by neural network clustering. Then, we construct a features tensor according to latent influential user’s opinion, activity, and network centrality information. Furthermore, user influences are predicted by the latent factors resulting from the temporal restrained CP decomposition. Finally, we recommend microblog users considering both user influence and content similarity. Our experimental results show that the proposed model significantly improves recommendation performance. Meanwhile, the mean average precision of TFUC outperforms the baselines with 3.4% at least.

scholarly journals Documents Clustering Using K-Means Algorithm

2018 ◽  
Vol 3 (02) ◽  
Author(s):  
R.B Wahyu ◽  
Arnold Vito
Keyword(s):  
The Internet ◽  
Huge Number ◽  
Digital Era ◽  
Digital Storage ◽  
Wide Range ◽  
Content Similarity

<p>Nowadays in the digital era, people could easily access and stored a wide range of information through the Internet into documents. With the huge number of unstructured documents with various type of information in digital storage, people need an application that could help them organize and classify the documents automatically. Documents Clustering using K-Means Algorithm is a desktop-based documents clustering application which implement K-Means Algorithm to provides clustering output based on the documents content similarity up to 85% accuracy based on the user expectation.</p>

Recommendation Systems for Mobile Devices

2012 ◽  
pp. 221-242
Author(s):  
Fabrizio Caruso ◽  
Giovanni Giuffrida ◽  
Diego Reforgiato ◽  
Calogero Zarba
Keyword(s):  
Mobile Devices ◽  
Online Community ◽  
Collective Intelligence ◽  
Recommendation Systems ◽  
Similarity Criterion ◽  
Limited Bandwidth ◽  
Online Newspaper ◽  
Geographical Position ◽  
Content Similarity ◽  
Average User

The authors describe three recommendation systems for online articles that are specifically tailored for mobile devices. In order to increase the number of articles read by the average user, an online newspaper could be personalized for each reader. Each user receives a personalized selection of the articles that take into account the limited bandwidth and screen, the user’s preferences, and possibly their geographical position. Two general criteria are followed: a collective intelligence criterion and a content similarity criterion. The suggested articles need to be both popular among the members of the online community, and similar to the articles already read by the user. The three systems address three similar problems. NeoPage is a tool for newspapers’ editors that suggests the position that each article should have on a web page. ARS is a tool for newspaper readers, which recommends the most similar articles to an article just read. MyNews is a tool for the readers, which produces a list of recommended articles by taking into account both the popularity of the article and the previously read articles by the user.

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