Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Fang'ai Liu"'
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-13 (2022)
Abstract Click-through rate prediction, which aims to predict the probability of the user clicking on an item, is critical to online advertising. How to capture the user evolving interests from the user behavior sequence is an important issue in CTR
Externí odkaz:
https://doaj.org/article/f577435383d945bdad822d50155e6dd1
Publikováno v:
PLoS ONE, Vol 17, Iss 10 (2022)
Video question answering (Video-QA) is a subject undergoing intense study in Artificial Intelligence, which is one of the tasks which can evaluate such AI abilities. In this paper, we propose a Modality Attention Fusion framework with Hybrid Multi-he
Externí odkaz:
https://doaj.org/article/2d7ac731f1a849549b44bcfa03d0d5f0
Publikováno v:
PLoS ONE, Vol 17, Iss 8, p e0273048 (2022)
Click-through rate prediction has become a hot research direction in the field of advertising. It is important to build an effective CTR prediction model. However, most existing models ignore the factor that the sequence is composed of sessions, and
Externí odkaz:
https://doaj.org/article/1b92d29fcc004e09b85227213a3f7765
Publikováno v:
IEEE Access, Vol 7, Pp 12779-12789 (2019)
Click-through rate (CTR) prediction is critical in Internet advertising and affects web publisher's profits and advertiser's payment. In the CTR prediction, the interaction between features is a key factor affecting the prediction rate. The tradition
Externí odkaz:
https://doaj.org/article/b96626e3a1a3433ba880c36c54d05f24
Publikováno v:
IEEE Access, Vol 7, Pp 127754-127764 (2019)
Aspect-level sentiment classification (ASC) is a research hotspot in natural language processing, which aims to infer the sentiment polarity of a particular aspect in an opinion sentence. There are three main influence factors in the aspect-level sen
Externí odkaz:
https://doaj.org/article/9745f67f5f9041faa9c8108ede111a46
Publikováno v:
IEEE Access, Vol 6, Pp 66095-66104 (2018)
In social networks, influential spreaders are those nodes that can spread information to a large number of nodes. Identifying influential spreaders is a major challenge for applications, such as information diffusion acceleration, epidemic outbreak p
Externí odkaz:
https://doaj.org/article/0194becffb62469096b566aea3271d99
Publikováno v:
PLoS ONE, Vol 14, Iss 9, p e0221271 (2019)
Identification of the most influential spreaders that maximize information propagation in social networks is a classic optimization problem, called the influence maximization (IM) problem. A reasonable diffusion model that can accurately simulate inf
Externí odkaz:
https://doaj.org/article/26c1ac12748741c2b0b413dc54db5755
Publikováno v:
ISPRS International Journal of Geo-Information, Vol 6, Iss 2, p 35 (2017)
Influential nodes are rare in social networks, but their influence can quickly spread to most nodes in the network. Identifying influential nodes allows us to better control epidemic outbreaks, accelerate information propagation, conduct successful e
Externí odkaz:
https://doaj.org/article/f962ee15560b46c692a6aaacc66b5d96
Publikováno v:
Neural Processing Letters. 55:1293-1316
Publikováno v:
Electronics
Volume 12
Issue 5
Pages: 1223
Volume 12
Issue 5
Pages: 1223
In recent years, mining user multi-behavior information for prediction has become a hot topic in recommendation systems. Usually, researchers only use graph networks to capture the relationship between multiple types of user-interaction information a