https://hdl.handle.net/10481/13882 2026-04-26T02:44:50Z https://hdl.handle.net/10481/112842 Combining Content Information with Collaborative Filtering for Publication Venue Recommendation Campos Ibáñez, Luis Miguel; Fernández Luna, Juan Manuel; Huete Guadix, Juan Francisco This paper addresses the problem of academic venue recommendation by developing a hybrid collaborative filtering model that integrates both behavioral and content information. We propose two complementary strategies for incorporating textual content into the collaborative filtering process: enriching the definition of neighborhoods and enhancing the computation of ratings. To evaluate these approaches, we conduct experiments on two document collections, PMSC-UGR and CORD-19, and benchmark them against two state-of-the-art baselines: A publication-based model, which constructs neighborhoods from authors’ venue rating vectors, and a coauthorship-based model, which relies on shared publications to establish similarity. In addition, we explore alternative neighborhood definitions that capture author similarity through textual features, enabling the derivation of latent venue preferences. Experimental results show that integrating content information consistently improves recommendation quality, either by refining neighborhoods or by adjusting ratings. The findings also highlight the importance of adapting the role of textual content to the characteristics of the dataset, as well as the need to investigate richer text representations to mitigate redundancy effects observed when combining content in both components of the model. This research is part of the project PID2022-139293NB-C33, funded by the Spanish Ministerio de Ciencia, Innovación y Universidades, MICIU/AEI/10.13039/501100011033/, and the European Regional Development Fund (ERDF/EU). Funding for open access charge: Universidad de Granada / CBUA. https://hdl.handle.net/10481/112804 Double Roman Domination Problem: An iterated local search approach Casado, Alejandra; Sánchez Oro, Jesús; Cordón García, Óscar In the last few decades, graph domination problems have attracted the attention of both academics and practitioners. In these problems, a subset of vertices is selected such that every vertex in the graph is either in the subset or adjacent to at least one selected vertex. One of the most extended variants is the Roman Domination Problem (RDP), where vertices are assigned values to ensure coverage under specific protection rules. This research addresses the Double Roman Domination Problem (DROMDP), a more restrictive extension of RDP in which stronger domination conditions are imposed to guarantee coverage even under potential vertex failures. In this paper, an algorithm based on the Iterated Local Search (ILS) framework is proposed, considering the use of two constructive procedures, two local search methods, and two perturbation mechanisms to find high-quality solutions. The results obtained are compared with the state-of-the-art method, based on Ant Colony Optimization, with ILS emerging as the most competitive algorithm for DROMDP. These results are supported by an extensive computational experimentation, including an ablation study of the different components, statistical tests, and a Bayesian analysis on the probability of ILS for being the best algorithm for any instance. https://hdl.handle.net/10481/112788 A Domain-Based Taxonomy of Jailbreak Vulnerabilities in Large Language Models Peláez-González, Carlos; Herrera-Poyatos, Andrés; Zuheros, Cristina; Herrera-Poyatos, David; Tejedor, Virilo; Herrera Triguero, Francisco The study of large language models (LLMs) is a key area in open-world machine learning. Although LLMs demonstrate remarkable natural language processing capabilities, they also face several challenges, including consistency issues, hallucinations, and jailbreak vulnerabilities. Jailbreaking refers to the crafting of prompts that bypass alignment safeguards, leading to unsafe outputs that compromise the integrity of LLMs. This work specifically focuses on the challenge of jailbreak vulnerabilities and introduces a novel taxonomy of jailbreak attacks grounded in the training domains of LLMs. It characterizes alignment failures as arising from gaps in generalization, objectives, and robustness. Our primary contribution is a perspective on jailbreak, framed through the different linguistic domains that emerge during LLM training and alignment. This viewpoint highlights the limitations of existing approaches and enables us to classify jailbreak attacks in terms of the underlying model deficiencies they exploit. Unlike conventional classifications that categorize attacks based on prompt construction methods (e.g., prompt templating), our approach provides a deeper understanding of LLM behavior. We introduce a taxonomy with four categories —mismatched generalization, competing objectives, adversarial robustness, and mixed attacks— offering insights into the fundamental nature of jailbreak vulnerabilities. Finally, we present key lessons derived from this taxonomic study. This research results from the Strategic Project IAFERCib (C074/23), as a result of the collaboration agreement signed between the National Institute of Cybersecurity (INCIBE) and the University of Granada. This initiative is carried out within the framework of the Recovery, Transformation and Resilience Plan funds, financed by the European Union (Next Generation). https://hdl.handle.net/10481/112666 Social network of peer-to-peer accommodations for a visual decision support system in tourism: The case of the Canary Islands Vargas Pérez, Víctor Alejandro; Cordón García, Oscar; Chica Serrano, Manuel; Hernández Guerra, Juan María The peer-to-peer accommodation market has experienced significant growth in recent years, leading to increased competition and offer heterogeneity. This scenario presents challenges for investors and stakeholders, required to value the importance of differentiation and accommodations' typology to ensure favorable profits and social impact. In this work, we examine the touristic accommodation market in the Canary Islands using real data from Airbnb and applying a novel network-based visual methodology. The data analysis methodology involves the creation and visualization of a network that places accommodations based on their similarity. Using community detection algorithms, we identify accommodation typologies, perform a descriptive analysis of the resulting clusters, and evaluate economic and exogenous variables. Nine accommodation types are found having key differentiating characteristics such as guest capacity, number of properties owned by the host, and managerial aspects (for example, cancellation policy). Clusters with higher economic benefits (characterized by a large capacity) are placed on the periphery of the visual map in contrast to common accommodation types, located in the center; thus showing the importance of differentiation. The accommodations' typologies are not specific to a particular island, but are homogeneously distributed in the Canaries archipelago. The results emphasize the managerial advantage of this decision support system for investors and tourist managers in making informed strategic decisions. This work was supported by MCIN/AEI/10.13039/501100011033 and ERDF “A way of making Europe” under grant CONFIA (PID2021-122916NB-I00); and by Ministerio de Industria y Turismo of the Spanish Government under grant TUR-RETOS2022-075. V. Vargas is also supported through the FPU program (FPU20/02441). M. Chica is also supported by grant EMERGIA21_00139 funded by Consejería de Universidad, Investigación e Innovación of the Andalusian Government. https://hdl.handle.net/10481/112651 Opinion Dynamics with Highly Oscillating Opinions Vargas Pérez, Víctor Alejandro; Giráldez-Cru, Jesús; Cordón García, Óscar Opinion Dynamics (OD) models are a class of agent-based models that describe how opinions evolve within a population. In these models, individual opinions change through interactions governed by an opinion fusion rule that specifies how updates occur. Despite their simplicity, OD models offer interpretable mechanisms for understanding the collective dynamics of opinion formation. However, most existing approaches focus on the emergence of consensus, fragmentation, or polarization, while overlooking real-world scenarios characterized by highly oscillatory trends. This study addresses this limitation by evaluating the ability of several OD models extended with dynamic parameters to reproduce oscillatory dynamics. To this end, we formulate an optimization problem solved via evolutionary algorithms. The methodology is first validated on synthetic target series to assess the intrinsic oscillatory capabilities of the models, and subsequently applied to a real-world dataset of public opinion about immigration, drawn from the monthly barometer of the Spanish Sociological Research Center. Results show that the Agent-independent Time-based Bounded Confidence and Repulsion (ATBCR) model, which combines confidence-based and polarization-based update mechanisms, achieves the best performance. The optimized model closely reproduces historical opinion fluctuations while exhibiting interpretable, human-like patterns of collective evolution.