Zobrazeno 1 - 10
of 900
pro vyhledávání: '"Data Sparsity"'
Publikováno v:
Complex & Intelligent Systems, Vol 11, Iss 1, Pp 1-28 (2024)
Abstract The field of social network analysis has identified User Alignment (UA) as a crucial area of investigation. The objective of UA is to identify and connect user accounts across diverse social networks, even when there are no explicit intercon
Externí odkaz:
https://doaj.org/article/8a9c532e938b4bd0b0b9aee8c3893460
Publikováno v:
Journal of King Saud University: Computer and Information Sciences, Vol 36, Iss 9, Pp 102224- (2024)
With the development of artificial intelligence in education, knowledge tracing (KT) has become a current research hotspot and is the key to the success of personalized instruction. However, data sparsity remains a significant challenge in the KT dom
Externí odkaz:
https://doaj.org/article/f262d029c0fb4d1f9796c03cc8ccc987
Publikováno v:
Electronic Research Archive, Vol 32, Iss 4, Pp 2728-2744 (2024)
Point-of-interest (POI) recommendation has attracted great attention in the field of recommender systems over the past decade. Various techniques, such as those based on matrix factorization and deep neural networks, have demonstrated outstanding per
Externí odkaz:
https://doaj.org/article/b5e7a8917e594a29b040cf7f83201b50
Autor:
Xiaoyan Meng
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract Given the challenges of inter-domain information fusion and data sparsity in collaborative filtering algorithms, this paper proposes a cross-domain information fusion matrix decomposition algorithm to enhance the accuracy of personalized rec
Externí odkaz:
https://doaj.org/article/fe762410d6884a07919aee0eea412d9d
Autor:
Aljunid, Mohammed Fadhel a, b, ⁎, D.H., Manjaiah c, Hooshmand, Mohammad Kazim c, Ali, Wasim A. d, Shetty, Amrithkala M. c, e, Alzoubah, Sadiq Qaid c
Publikováno v:
In Neurocomputing 7 February 2025 617
Publikováno v:
IEEE Access, Vol 12, Pp 139524-139539 (2024)
Recommender systems are essential tools that provide personalized user experiences across various domains such as e-commerce, entertainment, social media, education and content streaming. The integration of auxiliary information, including user demog
Externí odkaz:
https://doaj.org/article/f941f414eac94180a169374116f1cf86
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 15052-15066 (2024)
In the realm of urban development, the precise classification and identification of land types are crucial for improving land use efficiency. This article proposes a land recognition and classification method based on data sparsity and improved Soft
Externí odkaz:
https://doaj.org/article/1b633eeee3fb49aebf7caed680aaaadf
Autor:
Adeel Ashraf Cheema, Muhammad Shahzad Sarfraz, Muhammad Usman, Qamar Uz Zaman, Usman Habib, Ekkarat Boonchieng
Publikováno v:
IEEE Access, Vol 12, Pp 102111-102125 (2024)
Recommender systems are crucial in today’s digital world, by enhancing user engagement experience in digital ecosystems. Internet of things (IoT) have huge potential to generate dynamic and real time data. The data generated through IoT are being u
Externí odkaz:
https://doaj.org/article/2b2febe1a0d34c28b49058d59a4a5984
Publikováno v:
Complex & Intelligent Systems, Vol 10, Iss 2, Pp 3119-3132 (2024)
Abstract Recommender system always suffers from various recommendation biases, seriously hindering its development. In this light, a series of debias methods have been proposed in the recommender system, especially for two most common biases, i.e., p
Externí odkaz:
https://doaj.org/article/a2acfcd37f724b5ea542f3627945955f
Autor:
Amutha S., Vikram Surya R.
Publikováno v:
Cybernetics and Information Technologies, Vol 23, Iss 4, Pp 51-62 (2023)
One of the methods most frequently used to recommend films is collaborative filtering. We examine the potential of collaborative filtering in our paper’s discussion of product suggestions. In addition to utilizing collaborative filtering in a new a
Externí odkaz:
https://doaj.org/article/3355de4a210b4090b9ea1b03b767aa40