Titlebook: Database Systems for Advanced Applications; 27th International C Arnab Bhattacharya,Janice Lee Mong Li,Rage Uday Ki Conference proceedings

西瓜

https://doi.org/10.1007/978-94-009-6268-2hanism is used to aggregate the social influence of users on the target user and the correlative items’ influence on a given item. The final latent factors of user and item are combined to make a prediction. Experiments on three real-world recommender system datasets validate the effectiveness of GN

tic-douloureux

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/263437.jpg

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GENUS

https://doi.org/10.1007/978-981-19-9542-2mmendation list is crucial for user-oriented applications. Many knowledge-based approaches combine graph neural networks with exploring node structural similarity, while paying little attention to semantically distinguishing potential user interests and item attributes. Therefore, personalized node

aneurysm

inchoate

César Fernández-de-las-Peñas,Kimberly Bensentworks (GNN) are widely applied to SBR due to their superiority on learning better item and session embeddings. However, existing GNN-based SBR models mainly leverage direct neighbors, lacking efficient utilization of multi-hop neighbors information. To address this issue, we propose a multi-head gr

Myosin

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Occlusal Diagnosis and Treatment of TMDems yet neglect tail ones, which are actually the focus of novel recommendation since they can provide more surprises for users and more profits for enterprises. Furthermore, current novelty oriented methods treat all users equally without considering their personal preference on popular or tail ite

Ondines-curse

Neuropeptides

Larry Wolford,Jacinto Fernandez Sanromanmethods focus on incorporating the semantic collaborative information of social friends. In this paper, we argue that the semantic strength of their friends is also influenced by the subnetwork structure of friendship groups, which had not been well addressed in social recommendation literature. We