Explanipedia

Gotta embed them all! - knowledge-aware recommendations fusing heterogeneous multimodal item embeddings Open

Giuseppe Spillo, Elio Musacchio, Cataldo Musto, Marco de Gemmis, Pasquale Lops , et al. · 2025

In this paper, we present a methodology to provide users with knowledge-aware recommendations based on the fusion of multimodal item embeddings. Our approach relies on the intuition that each modality ( i.e., graph, text, video, images, et…

DistillRecDial: A Knowledge-Distilled Dataset Capturing User Diversity in Conversational Recommendation Open

Alessandro Francesco Maria Martina, Alessandro Petruzzelli, Cataldo Musto, Marco de Gemmis, Pasquale Lops , et al. · 2025

Conversational Recommender Systems (CRSs) facilitate item discovery through multi-turn dialogues that elicit user preferences via natural language interaction. This field has gained significant attention following advancements in Natural L…

See the Movie, Hear the Song, Read the Book: Extending MovieLens-1M, Last.fm-2K, and DBbook with Multimodal Data Open

Giuseppe Spillo, Elio Musacchio, Cataldo Musto, Marco de Gemmis, Pasquale Lops , et al. · 2025

Empowering Recommender Systems based on Large Language Models through Knowledge Injection Techniques Open

Alessandro Petruzzelli, Cataldo Musto, Marco de Gemmis, Giovanni Semeraro, Pasquale Lops · 2025

7th Workshop on Explainable User Models and Personalised Systems (ExUM 2025) Open

Cataldo Musto, Marco Polignano, Amon Rapp, Giovanni Semeraro, Jürgen Ziegler · 2025

PHaSE Project - Promoting Healthy and Sustainable Eating through Interactive and Explainable AI Methods Open

Cataldo Musto, Amon Rapp, Ludovico Boratto · 2025

Sixth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS) Open

Vito Walter Anelli, Antonio Ferrara, Cataldo Musto, Fedelucio Narducci, Azzurra Ragone , et al. · 2024

Reproducibility of LLM-based Recommender Systems: the Case Study of P5 Paradigm Open

Pasquale Lops, Antonio Silletti, Marco Polignano, Cataldo Musto, Giovanni Semeraro · 2024

Recommender systems can significantly benefit from the availability of pre-trained large language models (LLMs), which can serve as a basic mechanism for generating recommendations based on detailed user and item data, such as text descrip…

Towards Green Recommender Systems: Investigating the Impact of Data Reduction on Carbon Footprint and Algorithm Performances Open

Giuseppe Spillo, Allegra De Filippo, Cataldo Musto, Michela Milano, Giovanni Semeraro · 2024

This work investigates the path toward green recommender systems by examining the impact of data reduction on both model performance and carbon footprint. In the pursuit of developing energy-efficient recommender systems, we investigated w…

Recommending Healthy and Sustainable Meals exploiting Food Retrieval and Large Language Models Open

Alessandro Petruzzelli, Cataldo Musto, Michele Ciro Di Carlo, Giovanni Tempesta, Giovanni Semeraro · 2024

Given the rising global concerns about healthy nutrition and environmental sustainability, individuals need more and more support in making good choices concerning their daily meals. To this end, in this paper we introduce HeaSE, a framewo…

Instructing and Prompting Large Language Models for Explainable Cross-domain Recommendations Open

Alessandro Petruzzelli, Cataldo Musto, L. Laraspata, Ivan Rinaldi, Marco de Gemmis , et al. · 2024

In this paper, we present a strategy to provide users with explainable cross-domain recommendations (CDR) that exploits large language models (LLMs). Generally speaking, CDR is a task that is hard to tackle, mainly due to data sparsity iss…

Recommender systems based on neuro-symbolic knowledge graph embeddings encoding first-order logic rules Open

Giuseppe Spillo, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro · 2024

In this paper, we present a knowledge-aware recommendation model based on neuro-symbolic graph embeddings that encode first-order logic rules . Our approach is based on the intuition that is the basis of neuro-symbolic AI systems: to combi…

Evaluating Content-based Pre-Training Strategies for a Knowledge-aware Recommender System based on Graph Neural Networks Open

Giuseppe Spillo, Francesco BOTTALICO, Cataldo Musto, Marco de Gemmis, Pasquale Lops , et al. · 2024

In this paper, we introduce a Knowledge-aware Recommender System (KARS) based on Graph Neural Networks that exploit pre-trained content-based embeddings to improve the representation of users and items. Our approach relies on the intuition…

Improving Transformer-based Sequential Conversational Recommendations through Knowledge Graph Embeddings Open

Alessandro Petruzzelli, Alessandro Francesco Maria Martina, Giuseppe Spillo, Cataldo Musto, Marco de Gemmis , et al. · 2024

Conversational Recommender Systems (CRS) have recently drawn attention due to their capacity of delivering personalized recommendations through multi-turn natural language interactions. In this paper, we fit into this research line and we …

GInRec: A Gated Architecture for Inductive Recommendation using Knowledge Graphs Open

Theis E. Jendal, Matteo Lissandrini, Peter Dolog, Katja Hose, Vito Walter Anelli , et al. · 2023

We have witnessed increasing interest in exploiting KGs to integrate contextual knowledge in recommender systems in addition to user-item interactions, e.g., ratings. Yet, most methods are transductive, i.e., they represent instances seen …

“Tell Me Why”: using natural language justifications in a recipe recommender system to support healthier food choices Open

Alain D. Starke, Cataldo Musto, Amon Rapp, Giovanni Semeraro, Christoph Trattner · 2023

Harnessing distributional semantics to build context-aware justifications for recommender systems Open

Cataldo Musto, Giuseppe Spillo, Giovanni Semeraro · 2023

This paper introduces a methodology to generate review-based natural language justifications supporting personalized suggestions returned by a recommender system. The hallmark of our strategy lies in the fact that natural language justific…

Fifth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS) Open

Vito Walter Anelli, Pierpaolo Basile, Gerard de Melo, Francesco M. Donini, Antonio Ferrara , et al. · 2023

The success of graph neural network-based models (GNNs) has significantly\nadvanced recommender systems by effectively modeling users and items as a\nbipartite, undirected graph. However, many original graph-based works often\nadopt result…

ClayRS: An end-to-end framework for reproducible knowledge-aware recommender systems Open

Pasquale Lops, Marco Polignano, Cataldo Musto, Antonio Silletti, Giovanni Semeraro · 2023

Knowledge-aware recommender systems represent one of the most innovative research directions in the area of recommender systems, aiming at giving meaning to information expressed in natural language and obtaining a deeper comprehension of …

The URW-KG: a Resource for Tackling the Underrepresentation of non-Western Writers Open

Marco Antonio Stranisci, Giuseppe Spillo, Cataldo Musto, Viviana Patti, Rossana Damiano · 2022

Digital media have enabled the access to unprecedented literary knowledge. Authors, readers, and scholars are now able to discover and share an increasing amount of information about books and their authors. Notwithstanding, digital archiv…

The URW-KG: a Resource for Tackling the Under-Representation of non-Western Writers Open

Marco Antonio Stranisci, Giuseppe Spillo, Cataldo Musto, Viviana Patti, Rossana Damiano · 2022

Digital media have enabled the access to an unprecedented literary knowledge. Authors, readers, and scholars are now able to discover and share an increasing amount of information about books and their authors. Notwithstanding, digital arc…

The URW-KG: a Resource for Tackling the Under-Representation of non-Western Writers Open

Marco Antonio Stranisci, Giuseppe Spillo, Cataldo Musto, Viviana Patti, Rossana Damiano · 2022

<p>Digital media have enabled the access to an unprecedented literary knowledge. Authors, readers, and scholars are now able to discover and share an increasing amount of information about books and their authors. Notwithstanding, di…

The URW-KG: a Resource for Tackling the Under-Representation of non-Western Writers Open

Marco Antonio Stranisci, Giuseppe Spillo, Cataldo Musto, Viviana Patti, Rossana Damiano · 2022

Digital media have enabled the access to an unprecedented literary knowledge. Authors, readers, and scholars are now able to discover and share an increasing amount of information about books and their authors. Notwithstanding, digital arc…

Tell me What you Like: Introducing Natural Language Preference Elicitation Strategies in a Virtual Assistant for the Movie Domain Open

Cataldo Musto, Alessandro Francesco Maria Martina, Andrea Iovine, Fedelucio Narducci, Marco de Gemmis , et al. · 2022

Preference elicitation is a crucial step for every recommendation algorithm. Traditional interaction strategies for eliciting users’ interests and needs range from button-based interfaces, where users have to select what they like among a …

FinRec: The 3rd International Workshop on Personalization & Recommender Systems in Financial Services Open

Toine Bogers, Cataldo Musto, David Wang, Alexander Felfernig, Simone Borg Bruun , et al. · 2022

The FinRec workshop series offers a central forum for the study and discussion of the domain-specific aspects, challenges, and opportunities of RecSys and other related technologies in the financial services domain. Six years after the sec…

Harnessing Distributional Semantics to Build Context-Aware Justifications for Recommender Systems Open

Cataldo Musto, Giuseppe Spillo, Giovanni Semeraro · 2022

Tell Me What You Like: Introducing Natural Language Preference Elicitation Strategies in a Virtual Assistant for the Movie Domain Open

Cataldo Musto, Alessandro Francesco Maria Martina, Andrea Iovine, Marco de Gemmis, Giovanni Semeraro · 2022

<span>MyrrorBot</span> : A Digital Assistant Based on Holistic User Models for Personalized Access to Online Services Open

Cataldo Musto, Fedelucio Narducci, Marco Polignano, Marco de Gemmis, Pasquale Lops , et al. · 2021

In this article, we present MyrrorBot , a personal digital assistant implementing a natural language interface that allows the users to: (i) access online services, such as music, video, news, and food recommendation s, in a personalized w…

Workshop on Explainable User Models and Personalized Systems (ExUM 2021) Open

Cataldo Musto, Nava Tintarev, Oana Inel, Marco Polignano, Giovanni Semeraro , et al. · 2021

Adaptive and personalized systems have become pervasive technologies that are gradually playing an increasingly important role in our daily lives. Indeed, we are now used to interact every day with algorithms that help us in several scenar…

Exploring the Effects of Natural Language Justifications in Food Recommender Systems Open

Cataldo Musto, Alain D. Starke, Christoph Trattner, Amon Rapp, Giovanni Semeraro · 2021

Users of food recommender systems typically prefer popular recipes, which tend to be unhealthy. To encourage users to select healthier recommendations by making more informed food decisions, we introduce a methodology to generate and prese…

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