Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Nima Joorabloo"'
Publikováno v:
IEEE Access, Vol 8, Pp 202122-202132 (2020)
Collaborative Filtering (CF) approaches have been widely used in various applications of recommender systems. These methods are based on estimating the similarity between users/items by analyzing the ratings provided by users. The existing methods ar
Externí odkaz:
https://doaj.org/article/51e2e489d739400c8f249a824d0a0e24
Publikováno v:
Information Sciences. 601:242-254
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4908e74c0ef7dd9d5163cfd9f4399c9d
https://doi.org/10.1016/j.ins.2022.03.068
https://doi.org/10.1016/j.ins.2022.03.068
Publikováno v:
Expert Systems with Applications. 187:115849
Recommender systems use intelligent algorithms to learn a user’s preferences and provide them relevant suggestions. Lack of sufficient ratings – also known as data sparsity problem – often results in poor recommendation performance. The existin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2824489f548a740a5aa4d9b847679abc
https://doi.org/10.1016/j.eswa.2021.115849
https://doi.org/10.1016/j.eswa.2021.115849
Publikováno v:
Expert Systems with Applications. 184:115485
Over the last few years, with significant growth of information on the web, users are swamped with a huge amount of information. Recommender system (RS) is an information retrieval technology that aims to provide relevant items to users by considerin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::53c317d813ec2b325a90bf82d1942e98
https://doi.org/10.1016/j.eswa.2021.115485
https://doi.org/10.1016/j.eswa.2021.115485
Publikováno v:
Engineering Applications of Neural Networks ISBN: 9783030202569
EANN
EANN
The last two decades have seen a surge of data on the Web which causes overwhelming users with huge amount of information. Recommender systems (RSs) help users to efficiently find desirable items among a pool of items. RSs often rely on collaborating
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c2e9f2fc571921e81b8bab465ef3ebc1
https://doi.org/10.1007/978-3-030-20257-6_13
https://doi.org/10.1007/978-3-030-20257-6_13
Publikováno v:
KDIR
Recommender systems have significant applications in both industry and academia. Neighbourhood-based collaborative Filtering methods are the most widely used recommenders in industrial applications. These algorithms utilize preferences of similar use
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d9969dbbfd4a01fc36b03ab270de937d
https://doi.org/10.5220/0008377205550560
https://doi.org/10.5220/0008377205550560
Publikováno v:
Knowledge-Based Systems. 192:105371
Recommender systems attempt to suggest information that is of potential interest to users helping them to quickly find information relevant to them. In addition to historical user–item interaction data, such as users’ ratings on items, social rec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ba85b8855745367eeb53e61134b0d791
https://doi.org/10.1016/j.knosys.2019.105371
https://doi.org/10.1016/j.knosys.2019.105371
Publikováno v:
ASONAM
Recommender systems aim to suggest relevant items to users among a large number of available items. They have been successfully applied in various industries, such as e-commerce, education and digital health. On the other hand, clustering approaches
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::695180031e9e7ec49f9cbbceb03fd1c3
https://doi.org/10.1109/asonam.2018.8508723
https://doi.org/10.1109/asonam.2018.8508723