Zobrazeno 1 - 10
of 786
pro vyhledávání: '"rating prediction"'
Publikováno v:
Frontiers in Physics, Vol 12 (2024)
Online tourism spot recommendations, as a key component of tourism services, aim to present travel options that align with users’ personal preferences. However, current recommendation systems often underperform due to the sparsity of tourism data a
Externí odkaz:
https://doaj.org/article/f5576aeb41e24e07971949ca8c86a159
Publikováno v:
Frontiers in Psychology, Vol 15 (2024)
Literary reading is an interactive process between a reader and a text that depends on a balance between cognitive effort and emotional rewards. By studying both the crucial features of the text and of the subjective reader reception, a better unders
Externí odkaz:
https://doaj.org/article/b8caaf1c550c4d30bb560bff16a5bf5e
Publikováno v:
International Journal of Emerging Markets, 2021, Vol. 18, Issue 10, pp. 3414-3436.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/IJOEM-01-2021-0106
Autor:
Thon-Da Nguyen
Publikováno v:
Automatika, Vol 65, Iss 1, Pp 58-72 (2024)
Sentiment analysis is critical for classifying users on social media and reviewing products through comments and reviews. At the same time, rating prediction is a popular and valuable topic in research on recommendation systems. This study improves t
Externí odkaz:
https://doaj.org/article/e98015afca5c4511af3378dc7f0fd7fd
Autor:
Falguni Roy, Mahamudul Hasan
Publikováno v:
Vietnam Journal of Computer Science, Vol 10, Iss 04, Pp 517-536 (2023)
Item−item collaborative filtering is a sub-type of a recommender system that applies the items’ similarities for recommending a new set of items to the user. Usually, a traditional recommender system utilizes items’ ratings given by the user fo
Externí odkaz:
https://doaj.org/article/e5fc55df091d488d8631c18b782b699c
Publikováno v:
Applied Sciences, Vol 14, Iss 18, p 8303 (2024)
In recent years, recommender systems—which provide personalized recommendations by analyzing users’ historical behavior to infer their preferences—have become essential tools across various domains, including e-commerce, streaming media, and so
Externí odkaz:
https://doaj.org/article/89429da3db4c415b918db0590211ae0d
Autor:
Lijuan Shen, Liping Jiang
Publikováno v:
PeerJ Computer Science, Vol 10, p e1858 (2024)
Managing user bias in large-scale user review data is a significant challenge in optimizing children’s book recommendation systems. To tackle this issue, this study introduces a novel hybrid model that combines graph convolutional networks (GCN) ba
Externí odkaz:
https://doaj.org/article/d2eb2a08b6274716b3dc05dc4df7c41b
Autor:
Öztürk, Gözde, author, Tanrisevdi, Abdullah, author
Publikováno v:
Advanced Research Methods in Hospitality and Tourism
Publikováno v:
Jisuanji kexue, Vol 50, Iss 3, Pp 129-138 (2023)
Heterogeneous information network(HIN) contains rich semantic information,and the use of HIN for rating prediction has become an important way to alleviate the problem of data sparsity in recommender systems.However,the traditional methods using meta
Externí odkaz:
https://doaj.org/article/a1c7e639cab04c4db73b7ce2e2db1ed5
Publikováno v:
Aslib Journal of Information Management, 2022, Vol. 74, Issue 6, pp. 1126-1150.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/AJIM-12-2021-0357