Recommender systems has witnessed a great shift by leveraging contextual information as an auxiliary resource to improve the recommendations quality. These recommendation problems are addressed as link prediction tasks within bipartite graphs, where user and item nodes are connected by edges labelled with binary values or rating information. In this paper, we introduce a new architecture: Dynamic Graph Attention Network with Adaptive Edge Attributes (DGAT-AEA). Comprising multiple layers of dynamic Graph Attention Networks, that is designed to efficiently handle contextual recommendations. Our method is distinguished by its ability to update user and item representations while adapting the attributes of the connections between them during learning. This enables the capture of complex relationships within user-item interactions using contextual information. By optimizing model parameters and adapting edges features according to the user-item-context relationship, our approach outperforms existing methods in terms of recommendation accuracy and novelty, as demonstrated by experiments on benchmark datasets.

Given the daily influx of scientific publications, researchers often face challenges in identifying relevant content amid the vast volume of available information, typically resorting to conventional methods like keyword searches or manual browsing. Utilizing a dataset comprising 1895 users and 3122 articles from the CI&T Deskdrop collection, as well as 7947 users and 25,975 articles from CiteULike-t, we examine the effectiveness of collaborative filtering and content-based and hybrid recommendation approaches in scientific literature recommendations. These methods automatically generate article suggestions by analyzing user preferences and historical behavior. Our findings, evaluated based on accuracy (Precision@K), ranking quality (NDCG@K), and novelty, reveal that the hybrid approach significantly outperforms other methods, tackling some challenges such as cold starts and sparsity problems. This research offers theoretical insights into recommendation model effectiveness and practical implications for developing tools that enhance content discovery and researcher productivity.