Attentive Auto-encoder for Content-Aware Music Recommendation

2022 ◽  
Author(s):  
Le Li ◽  
Dan Tao ◽  
Chenwang Zheng ◽  
Ruipeng Gao
Keyword(s):  
Music Recommendation ◽  
Content Aware

Image Seam Carving Based On Content Aware Resizing By Gradient Method

2016 ◽  
pp. 25
Author(s):  
مثيل عماد الدين ◽  
رنا محمد حسن
Keyword(s):  
Gradient Method ◽  
Seam Carving ◽  
Content Aware

Content-Aware Image Resizing Based on Hybrid Energy

2010 ◽  
Vol 33 (10) ◽  
pp. 2015-2021
Author(s):  
Li-Xing LEI
Keyword(s):  
Image Resizing ◽  
Hybrid Energy ◽  
Content Aware

Boosting Dark Images Using Content-Aware Algorithm through Channel Division

2013 ◽  
Author(s):  
M. Balasaraswathi ◽  
A. Yasmin ◽  
P. Vedasundaravinayagam ◽  
V. Nagarajan
Keyword(s):  
Content Aware

scholarly journals Echo chamber detection and analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Giacomo Villa ◽  
Gabriella Pasi ◽  
Marco Viviani
Keyword(s):  
Social Media ◽  
Community Detection ◽  
Virtual Communities ◽  
Selective Exposure ◽  
Social Needs ◽  
News Sources ◽  
Detection Strategy ◽  
Echo Chambers ◽  
Echo Chamber ◽  
Content Aware

AbstractSocial media allow to fulfill perceived social needs such as connecting with friends or other individuals with similar interests into virtual communities; they have also become essential as news sources, microblogging platforms, in particular, in a variety of contexts including that of health. However, due to the homophily property and selective exposure to information, social media have the tendency to create distinct groups of individuals whose ideas are highly polarized around certain topics. In these groups, a.k.a. echo chambers, people only "hear their own voice,” and divergent visions are no longer taken into account. This article focuses on the study of the echo chamber phenomenon in the context of the COVID-19 pandemic, by considering both the relationships connecting individuals and semantic aspects related to the content they share over Twitter. To this aim, we propose an approach based on the application of a community detection strategy to distinct topology- and content-aware representations of the COVID-19 conversation graph. Then, we assess and analyze the controversy and homogeneity among the different polarized groups obtained. The evaluations of the approach are carried out on a dataset of tweets related to COVID-19 collected between January and March 2020.

scholarly journals Neural Collaborative For Music Recommendation System

2021 ◽  
Vol 1071 (1) ◽  
pp. 012021
Author(s):  
Abba Suganda Girsang ◽  
Antoni Wibowo ◽  
Jason ◽  
Roslynlia
Keyword(s):  
Recommendation System ◽  
Music Recommendation ◽  
Music Recommendation System
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