Zobrazeno 1 - 10
of 236
pro vyhledávání: '"Davulcu, Hasan"'
NeSHFS: Neighborhood Search with Heuristic-based Feature Selection for Click-Through Rate Prediction
Click-through-rate (CTR) prediction plays an important role in online advertising and ad recommender systems. In the past decade, maximizing CTR has been the main focus of model development and solution creation. Therefore, researchers and practition
Externí odkaz:
http://arxiv.org/abs/2409.08703
Image memes have become a widespread tool used by people for interacting and exchanging ideas over social media, blogs, and open messengers. This work proposes to treat automatic image meme generation as a translation process, and further present an
Externí odkaz:
http://arxiv.org/abs/2004.14571
Autor:
Alvari, Hamidreza, Beigi, Ghazaleh, Sarkar, Soumajyoti, Ruston, Scott W., Corman, Steven R., Davulcu, Hasan, Shakarian, Paulo
Over the past few years, we have observed different media outlets' attempts to shift public opinion by framing information to support a narrative that facilitate their goals. Malicious users referred to as "pathogenic social media" (PSM) accounts are
Externí odkaz:
http://arxiv.org/abs/2001.04624
Autor:
Salehi, Amin, Davulcu, Hasan
Auto-encoders have emerged as a successful framework for unsupervised learning. However, conventional auto-encoders are incapable of utilizing explicit relations in structured data. To take advantage of relations in graph-structured data, several gra
Externí odkaz:
http://arxiv.org/abs/1905.10715
The wide and rapid adoption of deep learning by practitioners brought unintended consequences in many situations such as in the infamous case of Google Photos' racist image recognition algorithm; thus, necessitated the utilization of the quantified u
Externí odkaz:
http://arxiv.org/abs/1905.09509
Autor:
Salehi, Amin, Davulcu, Hasan
Community detection on social media has attracted considerable attention for many years. However, existing methods do not reveal the relations between communities. Communities can form alliances or engage in antagonisms due to various factors, e.g.,
Externí odkaz:
http://arxiv.org/abs/1807.03617
Web 2.0 helps to expand the range and depth of conversation on many issues and facilitates the formation of online communities. Online communities draw various individuals together based on their common opinions on a core set of issues. Most existing
Externí odkaz:
http://arxiv.org/abs/1805.04191
Community detection is a fundamental task in social network analysis. In this paper, first we develop an endorsement filtered user connectivity network by utilizing Heider's structural balance theory and certain Twitter triad patterns. Next, we devel
Externí odkaz:
http://arxiv.org/abs/1608.01771
Publikováno v:
Big Data & Cognitive Computing; Jun2024, Vol. 8 Issue 6, p60, 18p
In this paper, we propose an efficient and scalable low rank matrix completion algorithm. The key idea is to extend orthogonal matching pursuit method from the vector case to the matrix case. We further propose an economic version of our algorithm by
Externí odkaz:
http://arxiv.org/abs/1404.1377