We introduce a load-aware selective embedding-pruning method to be used in dense retrieval systems. Our method adapts the embedding dimensionality at inference time based on per-query deadlines and the current queue occupancy. Under light load, full-dimensional embeddings maximise effectiveness; as load increases, dimensionality is adaptively reduced to maintain throughput and avoid query drops. Our approach is orthogonal to the specific dimensionality reduction strategy employed and operates independently of the embedding model. We evaluate our approach using both PCA-reduced embeddings and nested Matryoshka embeddings. Empirical results show that our load-aware strategy consistently achieves a better effectiveness–efficiency trade-off than static dimensionality reduction baselines across varying load conditions. As a by-product of this study, our experiments show that under selective pruning, the PCA-based approach consistently outperforms Matryoshka, indicating that specialised multi-representation training is not strictly required for robust load balancing in dense retrieval. Our code is available at: https://github.com/mariadianacalugaru/selective_pruning.

Recent advancements in Graph Neural Networks (GNN) have facilitated their widespread adoption in various applications, including recommendation systems. GNNs have proven to be effective in addressing the challenges posed by recommendation systems by efficiently modeling graphs in which nodes represent users or items and edges denote preference relationships. However, current GNN techniques represent nodes by means of a single static vector, which may inadequately capture the intricate complexities of users and items. To overcome these limitations, we propose a solution integrating a cutting-edge model inspired by category theory: Sheaf4Rec. Unlike single vector representations, Sheaf Neural Networks and their corresponding Laplacians represent each node (and edge) using a vector space. Our approach takes advantage of this theory and results in a more comprehensive representation that can be effectively exploited during inference, providing a versatile method applicable to a wide range of graph-related tasks and demonstrating unparalleled performance. Our proposed model exhibits a noteworthy relative improvement of up to 8.53% on F1-Score@10 and an impressive increase of up to 11.29% on NDCG@10, outperforming existing state-of-the-art models such as Neural Graph Collaborative Filtering (NGCF), KGTORe and other recently developed GNN-based models. In addition to its superior predictive capabilities, Sheaf4Rec shows remarkable improvements in terms of efficiency: we observe substantial runtime improvements ranging from 2.5% up to 37% when compared to other GNN-based competitor models, indicating a more efficient way of handling information while achieving better performance. Code is available at https://github.com/antoniopurificato/Sheaf4Rec .

This workshop will explore the latest approaches to medical knowledge systems, with a focus on the synergy between large language models, retrieval-augmented generation, and foundation/agentic models. The workshop will promote interdisciplinary collaboration among researchers, practitioners, and clinicians to advance evidence-driven AI in healthcare. Topics will include knowledge-grounded question answering, biomedical document retrieval, multimodal clinical reasoning, personalization, safety, and the challenges of deploying AI in practice. With a strong emphasis on reproducibility, evaluation, and responsible application in clinical settings, the workshop will define the next frontier of knowledge-centric AI in medicine.

Upselling recommendations play a critical role in improving customer engagement and maximizing revenue in the telecommunications industry. However, real-world data on such interactions often presents unique challenges, including multiple recommendations per call and sparse customer feedback, which complicates the evaluation of recommender systems. Our review of the existing literature reveals a critical gap in publicly available datasets that reflect these challenges, limiting progress in developing and evaluating upselling strategies.This work introduces a novel dataset that captures these complexities, offering valuable insights into customer behavior and recommendation effectiveness. The dataset, derived from real-world interactions between customers and service providers, contains multiple recommendations provided in individual calls and sparse feedback, reflecting typical user behavior where interest may be low or unrecorded.To aid in the development of more effective recommendation systems, we provide detailed statistics on recommendation distributions, user engagement, and feedback patterns. Furthermore, we benchmark various recommendation models, from classical approaches to state-of-the-art neural networks, allowing for a comprehensive assessment of their recommendation accuracy in this challenging setting.

In recent years, Retrieval-Augmented Generation (RAG) systems have become a cornerstone of artificial intelligence, attracting considerable attention in a variety of fields. By integrating the strengths of information retrieval and generative models, these systems have shown immense potential to push the boundaries of machine learning applications. Nevertheless, RAG systems still face significant challenges and offer ample room for advancement and innovation.

Recommender systems based on Graph Neural Networks (GNN) have become the state-of-the-art approach in recommendation, but they struggle with in extreme cold-start settings, where most users or items lack interaction data. This paper proposes a novel framework to address this challenge in four steps: (i) a propensity model to predict item purchase behaviour, with associated explainability to identify the most relevant features, (ii) a link augmentation module to connect users based on previously obtained similarities, (iii) a GNN-based link prediction step on the obtained dense graph and (iv) a final re-ranking stage to increase diversity in predictions leveraging users embeddings. By exploiting the enriched graph structure, the framework generates embeddings for cold-start users and items, enabling diverse recommendations, containing long tail and unsold items, for both established and new users. We validate the framework's effectiveness on real-world industrial data from TIM S.p.A.

In recent years, recommender systems have become indispensable tools in various domains, aiding users in discovering relevant content amidst the overwhelming amount of available material. However, the effectiveness and reliability of these systems are often hindered by various challenges such as data perturbations, missing data, noise, and bias. In this workshop, we aim to explore and address these challenges by focusing on the development of robust recommender systems. Robustness in recommender systems refers to their ability to maintain performance and effectiveness under adverse conditions, including unexpected variations in the data environment. By fostering discussions and collaborations among researchers and practitioners, this workshop seeks to advance the state-of-the-art in robust recommender systems, thereby enhancing their usability and trustworthiness in real-world applications.

Retrieval-Augmented Generation (RAG) has recently emerged as a method to extend beyond the pre-trained knowledge of Large Language Models by augmenting the original prompt with relevant passages or documents retrieved by an Information Retrieval (IR) system. RAG has become increasingly important for Generative AI solutions, especially in enterprise settings or in any domain in which knowledge is constantly refreshed and cannot be memorized in the LLM. We argue here that the retrieval component of RAG systems, be it dense or sparse, deserves increased attention from the research community, and accordingly, we conduct the first comprehensive and systematic examination of the retrieval strategy of RAG systems. We focus, in particular, on the type of passages IR systems within a RAG solution should retrieve. Our analysis considers multiple factors, such as the relevance of the passages included in the prompt context, their position, and their number. One counter-intuitive finding of this work is that the retriever's highest-scoring documents that are not directly relevant to the query (e.g., do not contain the answer) negatively impact the effectiveness of the LLM. Even more surprising, we discovered that adding random documents in the prompt improves the LLM accuracy by up to 35%. These results highlight the need to investigate the appropriate strategies when integrating retrieval with LLMs, thereby laying the groundwork for future research in this area.

In recent years, Retrieval Augmented Generation (RAG) systems have emerged as a pivotal component in the field of artificial intelligence, gaining significant attention and importance across various domains. These systems, which combine the strengths of information retrieval and generative models, have shown promise in enhancing the capabilities and performance of machine learning applications. However, despite their growing prominence, RAG systems are not without their limitations and continue to be in need of exploration and improvement. This workshop seeks to focus on the critical aspect of information retrieval and its integral role within RAG frameworks. We argue that current efforts have undervalued the role of Information Retrieval (IR) in the RAG and have concentrated their attention on the generative part. As the cornerstone of these systems, IR's effectiveness dramatically influences the overall performance and outcomes of RAG models. We call for papers that will seek to revisit and emphasize the fundamental principles underpinning RAG systems. At the end of the workshop, we aim to have a clearer understanding of how robust information retrieval mechanisms can significantly enhance the capabilities of RAG systems. The workshop will serve as a platform for experts, researchers, and practitioners. We intend to foster discussions, share insights, and encourage research that underscores the vital role of Information Retrieval in the future of generative systems.

Sequential Recommender Systems (SRSs) are a popular type of recommender system that leverages user history to predict the next item of interest. However, the presence of noise in user interactions, stemming from account sharing, inconsistent preferences, or accidental clicks, can significantly impact the robustness and performance of SRSs, particularly when the entire item set to be predicted is noisy. This situation is more prevalent when only one item is used to train and evaluate the SRSs. To tackle this challenge, we propose a novel approach that addresses the issue of noise in SRSs. First, we propose a sequential multi-relevant future items training objective, leveraging a loss function aware of item relevance, thereby enhancing their robustness against noise in the training data. Additionally, to mitigate the impact of noise at evaluation time, we propose multi-relevant future items evaluation (MRFI-evaluation), aiming to improve overall performance. Our relevance-aware models obtain an improvement of 1.58% of NDCG@10 and 0.96% in terms of HR@10 in the traditional evaluation protocol, the one which utilizes one relevant future item. In the MRFI-evaluation protocol, using multiple future items, the improvement is 2.82% of NDCG@10 and 0.64% of HR@10 w.r.t the best baseline model.