Simplices-based higher-order enhancement graph neural network for multi-behavior recommendation
Multi-behavior recommendations effectively integrate various types of behaviors and have
been proven to enhance recommendation performance. However, existing researches …
been proven to enhance recommendation performance. However, existing researches …
A novel deep learning approach toward efficient and accurate recommendation using improved alternating least squares in social media
The increasing number of electronic transactions on the Internet has given rise to the design
of recommendation systems. The main objective of these systems is to give …
of recommendation systems. The main objective of these systems is to give …
A cascaded embedding method with graph neural network for multi-behavior recommendation
S Jiang, C Zhao - International Journal of Machine Learning and …, 2024 - Springer
In recommender systems, implicit feedback data is relatively cheap and easy to obtain
compared to explicit feedback data, making it widely used in modeling. However, some …
compared to explicit feedback data, making it widely used in modeling. However, some …
Co-contrastive learning for multi-behavior recommendation
Multi-behavior recommender system (MBR) typically utilizes multi-typed user interactive
behaviors (eg, view, add-to-cart and purchase) in learning user preference on target …
behaviors (eg, view, add-to-cart and purchase) in learning user preference on target …
SCF: Structured collaborative filtering with heterogeneous implicit feedback
Recommendation systems aim to analyze users' historical behaviors to recommend items
that suit their preferences. In the real world, users' feedback is usually heterogeneous, such …
that suit their preferences. In the real world, users' feedback is usually heterogeneous, such …
Composition-enhanced graph collaborative filtering for multi-behavior recommendation
Rapid and accurate prediction of user preferences is the ultimate goal of today's
recommender systems. More and more researchers pay attention to multi-behavior …
recommender systems. More and more researchers pay attention to multi-behavior …
ASCM: Analysis of a Sequential and Collaborative Model for Recommendations
The recommender system can predict future lists of items based on the user's sentiments
and interactions. As the data is ubiquitous, we have a number of options available to make a …
and interactions. As the data is ubiquitous, we have a number of options available to make a …
Multi-behavior Recommendation with Hypergraph Contrastive Learning
B Yang, X Guo, L Shang, Z Zhang, Y Bi… - Chinese Conference on …, 2024 - Springer
In the rapidly expanding realm of multimedia data, information overload is an urgent issue
as users increasingly seek personalized experiences. Recommendation systems aim to …
as users increasingly seek personalized experiences. Recommendation systems aim to …
Noise-Enhanced Graph Contrastive Learning for Multimodal Recommendation Systems
K Shi, Y Zhang, M Zhang, K **ao, X Hou… - Available at SSRN … - papers.ssrn.com
Multimodal recommendation systems enhance recommendation accuracy through the
fusion of different types of information. Graph Contrastive Learning (GCL) has recently been …
fusion of different types of information. Graph Contrastive Learning (GCL) has recently been …
使用個性化多互動偏好排名的多行為推薦系統
吳偉樂 - 2023 - tdr.lib.ntu.edu.tw
多行為推薦的目標是利用用戶及物品的多交互關係例如購買和加入購物車來進行建模以解決
推薦中常見的資料稀疏及冷啟動問題. 雖然最**一些基於多行為的推薦演算法成功地利用不同 …
推薦中常見的資料稀疏及冷啟動問題. 雖然最**一些基於多行為的推薦演算法成功地利用不同 …