The application of recommendation technologies has been crucial in the promotion of physical and digital content across numerous global platforms such as Amazon, Apple, and Netflix. Our study aims to investigate the advantages of employing recommendation technologies on educational platforms, with a particular focus on an educational platform for learning and practicing music. Our research is based on data from Tomplay, a music platform that offers sheet music with professional audio recordings, enabling users to discover and practice music content at varying levels of difficulty. Through our analysis, we emphasize the distinct interaction patterns on educational platforms like Tomplay, which we compare with other commonly used recommendation datasets. We find that interactions are comparatively sparse on educational platforms, with users often focusing on specific content as they learn, rather than interacting with a broader range of material. Therefore, our primary goal is to address the issue of data sparsity. We achieve this through entity resolution principles and propose a neural network (NN)-based recommendation model. Further, we improve this model by utilizing graph neural networks (GNNs), which provide superior predictive accuracy compared to NNs. Notably, our study demonstrates that GNNs are highly effective even for users with little or no historical preferences (cold-start problem). Our cold-start experiments also provide valuable insights into an independent issue, namely, the number of historical interactions needed by a recommendation model to gain a comprehensive understanding of a user. Our findings demonstrate that a platform acquires a solid knowledge of a user’s general preferences and characteristics with 50 past interactions. Overall, our study makes significant contributions to information systems research on business analytics and prescriptive analytics. Moreover, our framework and evaluation results offer implications for various stakeholders, including online educational institutions, education policymakers, and learning platform users.

We investigate methods for manipulating classifier explanations while keeping the predictions unchanged. Our focus is on using a sparse attack, which seeks to alter only a minimal number of input features. We present an efficient and novel algorithm for computing sparse perturbations that alter the explanations but keep the predictions unaffected. We demonstrate that our algorithm, compared to PGD attacks with $\ell_{0}$ constraint, generates sparser perturbations while resulting in greater discrepancies between original and manipulated explanations. Moreover, we demonstrate that it is also possible to conceal the attribution of the k most significant features in the original explanation by perturbing fewer than k features of the input data. We present results for both image and tabular datasets, and emphasize the significance of sparse perturbation-based attacks for trustworthy model building in high-stakes applications. Our research reveals important vulnerabilities in explanation methods that should be taken into account when developing reliable explanation methods. Code can be found at https://github.com/ahmadajal/sparse_expl_attacks

Traditional embedding methodologies, also known as dimensionality reduction techniques, assume the availability of exact pairwise distances between the high-dimensional objects that will be embedded in a lower dimensionality. In this article, we propose an embedding that overcomes this limitation and can operate on pairwise distances that are represented as a range of lower and upper bounds. Such bounds are typically estimated when objects are compressed in a lossy manner, so our approach is highly applicable in the case of big compressed datasets. Our methodology can preserve multiple aspects of the original data relationships: distances, correlations, and object scores/ranks, whereas existing techniques typically preserve only distances. Comparative experiments with prevalent embedding methodologies (ISOMAP, t-SNE, MDS, UMAP) illustrate that our approach can provide fidelitous preservation of multiple object relationships, even in the presence of inexact distance information. Our visualization method is also easily interpretable.

Explanation methods highlight the importance of the input features in taking a predictive decision, and represent a solution to increase the transparency and trustworthiness in machine learning and deep neural networks (DNNs). However, explanation methods can be easily manipulated generating misleading explanations particularly under visually imperceptible adversarial perturbations. Recent work has identified the decision surface geometry of DNNs as the main cause of this phenomenon. To make explanation methods more robust against adversarially crafted perturbations, recent research has promoted several smoothing approaches. These approaches smooth either the explanation map or the decision surface.

Recommendation technologies have been playing an instrumental role for promoting both physical and digital content across several global platforms (Amazon, Apple, Netflix). Here we provide a study on the benefits of recommendation technologies in an educational platform with a focus on music learning. There are several characteristics present in this educational platform that make this recommendation problem particularly interesting, namely: a) the few but highly repetitive interactions, b) the existence of multiple versions of the same content across many difficulty levels, orchestrations, and musical instruments, and c) the user's expertise in a musical instrument which is essential for making appropriate recommendations. We highlight the unique dataset characteristics and compare them to those of other widely-used recommendation datasets. To alleviate the very high data sparsity due to the multi-instantiation of songs, we use entity resolution principles to embed songs in a new space. Using this lightweight entity resolution step on song data, in combination with neural recommendation architectures, we can double the predictive accuracy compared to techniques based on matrix factorization.

A common assumption in embedding methodologies is the availability of exact pairwise distances. In this paper, we propose a 2D embedding that overcomes this limitation. It can operate on distances that are represented as a range of lower and upper bounds. Such bounds are typically available when objects are compressed, whence our approach is highly applicable in the case of big compressed datasets. We establish linear convergence (i.e., exponential decay of distance to optimality) for the proposed scheme, with a rate characterized by the topology of the data graph. We compare with prevalent embedding methodologies (ISOMAP, t-SNE, MDS) and illustrate that our approach can provide fidelitous preservation of distances, correlations, and object ranks, even in the presence of inexact distance information.