Articles
World Wide Web (15731413)28(3)
A session-based recommendation system (SBRS) focuses on the user’s interactions in the current session to provide recommendations. Recent works employ graph neural networks (GNN) to capture complex relationships between clicked items in a session. Some of these studies incorporate collaborative information from similar/neighbor sessions. To identify neighbor sessions, they calculate the similarity of sessions by counting the common items between two sessions. But this is too simplistic and the similarity depends on other features such as the order of common items. Furthermore, meta-data such as items’ categories have not been considered, while items can be categorized into a limited number of categories, which can be informative in finding similar sessions and predicting user intent. In this paper, we propose a novel method, named Collaborative Category and Time-aware Graph Neural Networks (CCT-GNN), which models users’ interactions in two levels: (i) Global-level, which identifies neighbor sessions effectively and explores collaborative information to improve model performance. (ii) Local-level, in which the current session interactions are transformed into an item category graph to model different types of relations between items and categories. Experimental results demonstrate CCT-GNN superiority over state-of-the-art methods. Source code is available at: https://github.com/Moosazadeh/CCT-GNN. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.
Dadashi, D.,
Kaedi, M.,
Dadashi, P.,
Sinha ray, S. Molecular Informatics (18681751)44(2)
The widespread use of polymer solutions in the chemical industry poses a significant challenge in determining optimal dissolution conditions. Traditionally, researchers have relied on experimental methods to estimate the processing parameters needed to dissolve polymers, often requiring numerous iterations of testing different temperatures and pressures. This approach is both costly and time-consuming. In this study, for the first time, we present a machine learning-based approach to predict the minimum temperature and pressure required for polymer dissolution, correlating molecular weight and chemical structure of both the polymer and solvent and its weight percent. Using a dataset compiled from existing literature, which includes key factors influencing polymer dissolution, we also extracted chemical bond information from the molecular structures of polymer-solvent systems. Six different machine learning algorithms, including linear regression, k-nearest neighbors, regression trees, random forests, multilayer perceptron neural networks, and support vector regression, were employed to develop predictive models. Among these, the Random Forest model achieved the highest accuracy, with R2 values of 0.931 and 0.942 for temperature and pressure predictions, respectively. This novel approach eliminates the need for repetitive experimental testing, offering a more efficient pathway to determining dissolution conditions. © 2025 Wiley-VCH GmbH.
