• We stress the overlooked role of feature extraction and processing phase in the standard multimodal recommendation pipeline. • Research often follows limited experimental settings, ignoring powerful new extraction models with careful selection their hyper-parameters, new datasets from uncommon domains, and usually-untested modalities. • We provide a new, end-to-end framework for standardized benchmarking which, unlike other recent benchmarking studies in multimodal recommendation, incorporates Ducho (a feature extraction framework), and Elliot / MMRec (two popular frameworks for reproducible multimodal recommendation); we highlight the implementative challenges to make all these separate frameworks interact under the same experimental pipeline. • We run ∼ 4,000 experiments spanning 8 datasets, 8 multimodal extractors, 15 (multimodal) recommender systems, under 5 different experimental settings. • Our comprehensive pipeline allows extensive and diversified benchmarks in multimodal recommendation. We observe (in most cases) performance improvements with more recent multimodal extractors, whose outcomes are validated under various domains, modalities, and extractors hyper-parameters. With the advent of deep learning and, more recently, large models, recommendation systems have greatly refined their capability of profiling users’ preferences and interests that, in most cases, are complex to disentangle. This is especially true for those recommendation algorithms that rely heavily on external side information, such as multimodal recommender systems. In specific domains like fashion, music, and movie recommendation, the multi-faceted features characterizing products and services may influence each customer on online platforms differently, paving the way to novel multimodal recommendation models that can learn from such multimodal content. According to the literature, the common multimodal recommendation pipeline involves (i) extracting multimodal features, (ii) refining their high-level representations to suit the recommendation task, (iii) optionally fusing all multimodal features, and (iv) predicting the user-item score. Although great effort has been put into designing optimal solutions for (ii-iv), to the best of our knowledge, very little attention has been devoted to exploring procedures for (i) in a rigorous way. In this respect, the existing literature outlines the large availability of multimodal datasets and the ever-growing number of large models accounting for multimodal-aware tasks, but (at the same time) an unjustified adoption of limited standardized solutions. As very recent works from the literature have begun to conduct empirical studies to assess the contribution of multimodality in recommendation, we decide to follow and complement this same research direction. To this end, this paper settles as the first attempt to offer a large-scale benchmarking for multimodal recommender systems, with a specific focus on multimodal extractors. Specifically, we take advantage of three popular and recent frameworks for multimodal feature extraction and reproducibility in recommendation, Ducho , and MMRec / Elliot , respectively, to offer a unified and ready-to-use experimental environment able to run extensive benchmarking analyses leveraging novel multimodal feature extractors. Results, largely validated under different extractors, hyper-parameters of the extractors, domains, and modalities, provide important insights on how to train and tune the next generation of multimodal recommendation algorithms.

Recommendation pipelines involve several stages that can critically affect performance and reproducibility. However, early pipeline stages remain under-standardized, limiting comparability and interoperability across studies. This tutorial addresses this gap by providing both theoretical insights and hands-on experience with tools and practices for standardized data processing in recommender systems. In the first part, we introduce DataRec, a Python library for reproducible and interoperable data management, and discuss data filtering, splitting, and topological analysis techniques. In the second part, we explore multimodal feature extraction in domains such as fashion, music, and movies, focusing on the challenges of meaningful multimodal integration. We introduce Ducho, a unified framework for extracting audio, visual, and textual features using modern backends, and demonstrate its integration with the evaluation framework Elliot. The tutorial targets researchers and practitioners with an interest in recommender systems, data preprocessing, and multimodal modeling. All materials, including slides, code, datasets, and recordings, will be openly available on a dedicated tutorial website: https://sites.google.com/view/dd4rec-tutorial/.

Recommender systems have demonstrated a significant impact across diverse domains, yet ensuring the reproducibility of experimental findings remains a persistent challenge.A primary obstacle lies in the fragmented and often opaque data management strategies employed during the preprocessing stage, where decisions about dataset selection, filtering, and splitting can substantially influence outcomes.To address these limitations, we introduce DataRec, an open-source Python-based library specifically designed to unify and streamline data handling in recommender system research.By providing reproducible routines for dataset preparation, data versioning, and seamless integration with other frameworks, DataRec promotes methodological standardization, interoperability, and comparability across different experimental setups.Our design is informed by an in-depth review of 55 stateof-the-art recommendation studies, ensuring that DataRec adopts best practices while addressing common pitfalls in data management.Ultimately, our contribution facilitates fair benchmarking, enhances reproducibility, and fosters greater trust in experimental results within the broader recommender systems community.The DataRec library, documentation, and examples are freely available at https://github.com/sisinflab/DataRec.

Recommender systems have become essential tools for addressing information overload in the digital age. However, the collection and usage of user data for personalized recommendations raise significant privacy concerns. This research focuses on enhancing privacy in recommender systems through the application of differential privacy techniques, particularly in the domain of privacy-preserving data publishing.