X Peng, J Sun, M Yan, F Sun, F Wang - Knowledge-Based Systems, 2023 - Elsevier
Multi-behavior recommendation system aims to improve recommendation performance by using the interaction data of users′ multiple behaviors. Although some methods have …
Z Cheng, S Han, F Liu, L Zhu, Z Gao… - Proceedings of the ACM …, 2023 - dl.acm.org
Multi-behavior recommendation, which exploits auxiliary behaviors (eg, click and cart) to help predict users' potential interactions on the target behavior (eg, buy), is regarded as an …
B Jin, C Gao, X He, D Jin, Y Li - … of the 43rd international ACM SIGIR …, 2020 - dl.acm.org
Traditional recommendation models that usually utilize only one type of user-item interaction are faced with serious data sparsity or cold start issues. Multi-behavior recommendation …
Modern recommender systems often embed users and items into low-dimensional latent representations, based on their observed interactions. In practical recommendation …
Multi-types of user behavior data (eg, clicking, adding to cart, and purchasing) are recorded in most real-world recommendation scenarios, which can help to learn users' multi-faceted …
Q Li, H Ma, R Zhang, W Jin, Z Li - Applied Soft Computing, 2023 - Elsevier
Multi-behavior recommendation (MBR) aims to improve the prediction of the target behavior (ie, purchase) by exploiting multi-typed auxiliary behaviors, such as page view, cart and …
User purchasing prediction with multi-behavior information remains a challenging problem for current recommendation systems. Various methods have been proposed to address it via …
Accurate user and item embedding learning is crucial for modern recommender systems. However, most existing recommendation techniques have thus far focused on modeling …
S Fu, Q Ren, X Lv, J Li - Expert Systems with Applications, 2024 - Elsevier
Abstract Graph Neural Networks (GNNs) have been extensively employed in the field of recommendation systems, offering users personalized recommendations and yielding …