Zobrazeno 1 - 10
of 8 225
pro vyhledávání: '"Cold start"'
Publikováno v:
Jisuanji kexue yu tansuo, Vol 18, Iss 6, Pp 1600-1612 (2024)
In recent years, massive online open courses (MOOCs) platforms provide users with a wealth of learning resources. Nevertheless, information overload remains a pressing concern, necessitating the development of effective personalized course recommenda
Externí odkaz:
https://doaj.org/article/35c9af8b2f394857b29cc3315a4be636
Publikováno v:
Big Data Mining and Analytics, Vol 7, Iss 2, Pp 357-370 (2024)
Grid-based recommendation algorithms view users and items as abstract nodes, and the information utilised by the algorithm is hidden in the selection relationships between users and items. Although these relationships can be easily handled, much usef
Externí odkaz:
https://doaj.org/article/7b215583182f43d9b6e28d6b5d2673e7
Publikováno v:
Advanced Science, Vol 11, Iss 38, Pp n/a-n/a (2024)
Abstract Understanding the ice nucleation mechanism in the catalyst layers (CLs) of proton exchange membrane (PEM) fuel cells and inhibiting icing by designing the CLs can optimize the cold start strategies, which can enhance the performance of PEM f
Externí odkaz:
https://doaj.org/article/acbc3e497ca242b19b7c8e0815543528
Autor:
Hala Butmeh, Abdallatif Abu-Issa
Publikováno v:
Frontiers in Computer Science, Vol 6 (2024)
This article introduces a recommendation system that merges a knowledge-based (attribute-based) approach with collaborative filtering, specifically addressing the challenges of the pure-cold start scenario in personalized e-learning. The system gener
Externí odkaz:
https://doaj.org/article/df72c3e8309e4bb69a537b75d6083070
Publikováno v:
Smart Learning Environments, Vol 11, Iss 1, Pp 1-24 (2024)
Abstract The recommendation is an active area of scientific research; it is also a challenging and fundamental problem in online education. However, classical recommender systems usually suffer from item cold-start issues. Besides, unlike other field
Externí odkaz:
https://doaj.org/article/9a699b9861d04f73a58bee2038588062
Publikováno v:
Jisuanji kexue yu tansuo, Vol 18, Iss 5, Pp 1197-1210 (2024)
Recommender systems provide important functions in areas such as dealing with data overload, providing personalized consulting services, and assisting clients in investment decisions. However, the cold start problem in recommender systems has always
Externí odkaz:
https://doaj.org/article/ba7e441673af4d86810c7904b4ba325d
Autor:
Hyeji Oh, Chulyun Kim
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract Fairness has become a critical value online, and the latest studies consider it in many problems. In recommender systems, fairness is important since the visibility of items is controlled by systems. Previous fairness-aware recommender syste
Externí odkaz:
https://doaj.org/article/8b35b918ce10444c8df8b37e5fa74462
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
Publikováno v:
Data Science and Engineering, Vol 9, Iss 2, Pp 238-249 (2024)
Abstract The cold-start problem in recommender systems has been facing a great challenge. Cross-domain recommendation can improve the performance of cold-start user recommendations in the target domain by using the rich information of users in the so
Externí odkaz:
https://doaj.org/article/edc8b16f8d96431e9ec53fe2419fa094
Autor:
Ronakkumar Patel, Priyank Thakkar
Publikováno v:
Results in Engineering, Vol 24, Iss , Pp 103257- (2024)
The adoption of Recommender Systems (RSs) in various services is imperative in the present era. One of the inherent challenge faced by RSs is the user cold start problem (UCS) and it occurs when there is a lack of enough data available for a new user
Externí odkaz:
https://doaj.org/article/66edf10776654213ad03a7b37775a388