Zobrazeno 1 - 10
of 6 955
pro vyhledávání: '"Ming Zhong"'
Sequential recommendation leverages interaction sequences to predict forthcoming user behaviors, crucial for crafting personalized recommendations. However, the true preferences of a user are inherently complex and high-dimensional, while the observe
Externí odkaz:
http://arxiv.org/abs/2407.17802
Online marketing is critical for many industrial platforms and business applications, aiming to increase user engagement and platform revenue by identifying corresponding delivery-sensitive groups for specific incentives, such as coupons and bonuses.
Externí odkaz:
http://arxiv.org/abs/2406.00335
Autor:
Liu, Dugang, Xian, Shenxian, Lin, Xiaolin, Zhang, Xiaolian, Zhu, Hong, Fang, Yuan, Chen, Zhen, Ming, Zhong
The training paradigm integrating large language models (LLM) is gradually reshaping sequential recommender systems (SRS) and has shown promising results. However, most existing LLM-enhanced methods rely on rich textual information on the item side a
Externí odkaz:
http://arxiv.org/abs/2406.00333
In real recommendation scenarios, users often have different types of behaviors, such as clicking and buying. Existing research methods show that it is possible to capture the heterogeneous interests of users through different types of behaviors. How
Externí odkaz:
http://arxiv.org/abs/2402.12733
Cross-domain sequential recommendation is an important development direction of recommender systems. It combines the characteristics of sequential recommender systems and cross-domain recommender systems, which can capture the dynamic preferences of
Externí odkaz:
http://arxiv.org/abs/2401.15369
Publikováno v:
The Thirty-Third International Joint Conference on Artificial Intelligence Survey Track. 2024. Pages 7989-7998
Cross-domain sequential recommendation (CDSR) shifts the modeling of user preferences from flat to stereoscopic by integrating and learning interaction information from multiple domains at different granularities (ranging from inter-sequence to intra
Externí odkaz:
http://arxiv.org/abs/2401.04971
Recommender systems is set up to address the issue of information overload in traditional information retrieval systems, which is focused on recommending information that is of most interest to users from massive information. Generally, there is a se
Externí odkaz:
http://arxiv.org/abs/2308.15701
Publikováno v:
Journal of Intensive Medicine, Vol 4, Iss 4, Pp 453-467 (2024)
Sepsis is a life-threatening syndrome resulting from a dysregulated host response to infection. It is the primary cause of death in the intensive care unit, posing a substantial challenge to human health and medical resource allocation. The pathogene
Externí odkaz:
https://doaj.org/article/a97b8a3d845c452cb041e0865b42d325
Autor:
Hao Yuan, Ming Zhong, Jie Liu, Shuya Tang, Hongbo Zhu, Qingping Wei, Bingbing Pu, Yongping Li
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract Cytokine-induced apoptosis inhibitor 1 (CIAPIN1) is a protein that regulates apoptosis and programmed cell death. This research aims to evaluate its potential role in inhibiting breast cancer cell proliferation, migration, and glycolysis and
Externí odkaz:
https://doaj.org/article/552b122dcb354042b019bf6b3c423bf8
Graph neural networks (GNNs) have gained wide popularity in recommender systems due to their capability to capture higher-order structure information among the nodes of users and items. However, these methods need to collect personal interaction data
Externí odkaz:
http://arxiv.org/abs/2308.01197