2 citations · 2 across the 10 of their papers we have counts for
11 papers
Data Processing for Offline Evaluation in Recommender Systems: a Survey
Alberto Carlo Maria Mancino, Angela Di Fazio, Danilo Danese +5
Offline evaluation is the dominant experimental paradigm in recommender systems research, enabling reproducible and cost-effective comparisons on historical interaction data. Yet,…
PUMA: Post-Hoc Sparsification of Universal Multimodal Embeddings for Efficient Retrieval
Matteo Attimonelli, Alessandro De Bellis, Franco Maria Nardini +4
Universal multimodal embedders enable retrieval across text, image, and combined queries, but their dense representations incur high memory and inference costs. Post-hoc sparsifica…
Grounding Free-Form Instructions for Fashion Complementary Image Generation
Matteo Attimonelli, Claudio Pomo, Alessandro De Bellis +3
Fashion complementary image generation (CIG) aims to create garments that stylistically match a seed item based on user intent, making it a natural multimodal grounding problem whe…
WaveDiT: Distribution-Aware Wavelet Flow Matching for Efficient 3D Brain MRI Synthesis
Danilo Danese, Angela Lombardi, Giuseppe Fasano +2
Large and demographically balanced datasets are essential for reliable neuroimaging biomarkers. Full-resolution 3D brain MRI synthesis can support data augmentation in this setting…
Do Composed Image Retrieval Benchmarks Require Multimodal Composition?
Matteo Attimonelli, Alessandro De Bellis, Aryo Pradipta Gema +8
Composed Image Retrieval (CIR) is a multimodal retrieval task where a query consists of a reference image and a textual modification, and the goal is to retrieve a target image sat…
FlowLet: Conditional 3D Brain MRI Synthesis using Wavelet Flow Matching
Danilo Danese, Angela Lombardi, Matteo Attimonelli +2
Brain Magnetic Resonance Imaging (MRI) plays a central role in studying neurological development, aging, and diseases. One key application is Brain Age Prediction (BAP), which esti…
Do Recommender Systems Really Leverage Multimodal Content? A Comprehensive Analysis on Multimodal Representations for Recommendation
Claudio Pomo, Matteo Attimonelli, Danilo Danese +2
Multimodal Recommender Systems aim to improve recommendation accuracy by integrating heterogeneous content, such as images and textual metadata. While effective, it remains unclear…
Do We Really Need Specialization? Evaluating Generalist Text Embeddings for Zero-Shot Recommendation and Search
Matteo Attimonelli, Alessandro De Bellis, Claudio Pomo +3
Pre-trained language models (PLMs) are widely used to derive semantic representations from item metadata in recommendation and search. In sequential recommendation, PLMs enhance ID…
Fashion Image-to-Image Translation for Complementary Item Retrieval
Matteo Attimonelli, Claudio Pomo, Dietmar Jannach +1
The increasing demand for online fashion retail has boosted research in fashion compatibility modeling and item retrieval, focusing on matching user queries (textual descriptions o…
Large-scale Benchmarks for Multimodal Recommendation with Ducho
Matteo Attimonelli, Danilo Danese, Angela Di Fazio +3
The common multimodal recommendation pipeline involves (i) extracting multimodal features, (ii) refining their high-level representations to suit the recommendation task, (iii) opt…
Ducho 2.0: Towards a More Up-to-Date Unified Framework for the Extraction of Multimodal Features in Recommendation
Matteo Attimonelli, Danilo Danese, Daniele Malitesta +3
In this work, we introduce Ducho 2.0, the latest stable version of our framework. Differently from Ducho, Ducho 2.0 offers a more personalized user experience with the definition a…