M. Afshar Alam

Md. Tanvir Alam, Md. Ahasanul Alam, Md Mahmudur Rahman et al.

Relational databases (RDBs) are ubiquitous in enterprise and real-world applications. Flattening the database poses challenges for deep learning models that rely on fixed-size input representations to capture relational semantics from the structured nature of relational data. Graph neural networks (GNNs) have been proposed to address this, but they often oversimplify relational structures by modeling all the tuples as monolithic nodes and ignoring intra-tuple associations. In this work, we propose a novel hypergraph-based framework, that we call rel-HNN, which models each unique attribute-value pair as a node and each tuple as a hyperedge, enabling the capture of fine-grained intra-tuple relationships. Our approach learns explicit multi-level representations across attribute-value, tuple, and table levels. To address the scalability challenges posed by large RDBs, we further introduce a split-parallel training algorithm that leverages multi-GPU execution for efficient hypergraph learning. Extensive experiments on real-world and benchmark datasets demonstrate that rel-HNN significantly outperforms existing methods in both classification and regression tasks. Moreover, our split-parallel training achieves substantial speedups -- up to 3.18x for learning on relational data and up to 2.94x for hypergraph learning -- compared to conventional single-GPU execution.

Shahab Saquib Sohail, Jamshed Siddiqui, Rashid Ali et al.

The primary objective of this paper is to present an approach for recommender systems that can assimilate ranking to the voters or rankers so that recommendation can be made by giving priority to experts suggestion over usual recommendation. To accomplish this, we have incorporated the concept of human-centric aggregation via Ordered Weighted Aggregation (OWA). Here, we are advocating ranked recommendation where rankers are assigned weights according to their place in the ranking. Further, the recommendation process which is presented here for the recommendation of books to university students exploits linguistic data summaries and Ordered Weighted Aggregation (OWA) technique. In the suggested approach, the weights are assigned in a way that it associates higher weights to best ranked university. The approach has been evaluated over eight different parameters. The superiority of the proposed approach is evident from the evaluation results. We claim that proposed scheme saves storage spaces required in traditional recommender systems as well as it does not need users prior preferences and hence produce a solution for cold start problem. This envisaged that the proposed scheme can be very useful in decision making problems, especially for recommender systems. In addition, it emphasizes on how human-centric aggregation can be useful in recommendation researches, and also it gives a new direction about how various human specific tasks can be numerically aggregated.