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pro vyhledávání: '"cold start recommendation"'
Autor:
Luo, Yunze, Jiang, Yuezihan, Jiang, Yinjie, Chen, Gaode, Wang, Jingchi, Bian, Kaigui, Li, Peiyi, Zhang, Qi
With the rise of e-commerce and short videos, online recommender systems that can capture users' interests and update new items in real-time play an increasingly important role. In both online and offline recommendation, the cold-start problem due to
Externí odkaz:
http://arxiv.org/abs/2411.11225
For many recommender systems, the primary data source is a historical record of user clicks. The associated click matrix is often very sparse, as the number of users x products can be far larger than the number of clicks. Such sparsity is accentuated
Externí odkaz:
http://arxiv.org/abs/2404.13298
The cold start problem, where new users or items have no interaction history, remains a critical challenge in recommender systems (RS). A common solution involves using Knowledge Graphs (KG) to train entity embeddings or Graph Neural Networks (GNNs).
Externí odkaz:
http://arxiv.org/abs/2406.07420
Rating elicitation is a success element for recommender systems to perform well at cold-starting, in which the systems need to recommend items to a newly arrived user with no prior knowledge about the user's preference. Existing elicitation methods e
Externí odkaz:
http://arxiv.org/abs/2406.00973
Akademický článek
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Autor:
Lv, Xiaomin1 (AUTHOR) lvxiaomin@zjsru.edu.cn, Fang, Kai2 (AUTHOR) kaifang@zafu.edu.cn, Liu, Tongcun2 (AUTHOR) lvxiaomin@zjsru.edu.cn
Publikováno v:
Sensors (14248220). Sep2024, Vol. 24 Issue 17, p5510. 13p.
Publikováno v:
Application Research of Computers / Jisuanji Yingyong Yanjiu. Jul2024, Vol. 41 Issue 7, p2025-2032. 8p.
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
Collaborative Filtering (CF) recommender models highly depend on user-item interactions to learn CF representations, thus falling short of recommending cold-start items. To address this issue, prior studies mainly introduce item features (e.g., thumb
Externí odkaz:
http://arxiv.org/abs/2312.09901
Multimedia recommendation aims to fuse the multi-modal information of items for feature enrichment to improve the recommendation performance. However, existing methods typically introduce multi-modal information based on collaborative information to
Externí odkaz:
http://arxiv.org/abs/2307.02761