Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of natural language processing tasks, including code generation. While substantial progress has been made in adapting LLMs to generate code for various imperative programming languages, their effectiveness in handling declarative paradigms, such as Answer Set Programming (ASP), remains largely underexplored. This paper takes a step toward bridging that gap by investigating the potential of LLMs for ASP code generation. We begin with a systematic evaluation of several foundational LLMs, moving towards state-of-the-art models. We show that, despite their extensive training, large parameter counts, and significant computational backing, older models exhibit poor performance in generating syntactically and semantically correct ASP programs, while most recent ones mainly achieve impressive results. However, to overcome the need for huge computational power, we introduce LLASP, a fine-tuned, lightweight model specifically trained to encode ASP programs. In this regard, we extensively explore the effectiveness of fine-tuning by curating several dedicated datasets suitable for ASP encoding with increasing levels of complexity. First, we show that LLASP is effective in encoding template-based core problems in ASP; second, that the training strategy can be pushed forward to disregard the need for templating and make the generation prompt-invariant; and lastly, we show that even complex problems can be effectively encoded, beyond core tasks. Experimental results also show that LLASP significantly outperforms both its non-fine-tuned counterparts and most general-purpose LLMs, particularly in terms of semantic correctness, achieving a good trade-off between accuracy and resource-efficiency. Experimental code is publicly available at: https://github.com/EricaCoppolillo/LLASP .

Simulating a recommendation system in a controlled environment, to identify specific behaviors and user preferences, requires highly flexible synthetic data generation models capable of mimicking the patterns and trends of real datasets.In this context, we propose HyDRA, a novel preferences data generation model driven by three main factors: user-item interaction level, item popularity, and user engagement level.The key innovations of the proposed process include the ability to generate user communities characterized by similar item adoptions, reflecting real-world social influences and trends.Additionally, HyDRA considers item popularity and user engagement as mixtures of different probability distributions, allowing for a more realistic simulation of diverse scenarios.This approach enhances the model's capacity to simulate a wide range of real-world cases, capturing the complexity and variability found in actual user behavior.We demonstrate the effectiveness of HyDRA through extensive experiments on well-known benchmark datasets.The results highlight its capability to replicate real-world data patterns, offering valuable insights for developing and testing recommendation systems in a controlled and realistic manner.The code used to perform the experiments is publicly available: https://github.com/flexibledatageneration/HYDRA.

User navigation on social media platforms is often driven by recommendation algorithms . A growing body of literature questions whether these recommendation systems may exacerbate detrimental phenomena, perpetrate intrinsic biases, and alter user preferences in the long-term. Driven by this premise, the present study formalizes the concept of “ algorithmic drift ”, further introducing a novel framework and two metrics to quantify it. Our methodology involves a simulation process that models user behavior through random walks , reflecting user navigation under the influence and guidance of recommendation systems. This approach highlights that each user may respond differently to such stimuli, varying in both resistance to recommendation influence and inertia in selecting new steps in the random walk. The proposed metrics measure the drift in user behavior and item consumption over time in the random walks. We conduct a comprehensive evaluation over both synthetic and real-world datasets to validate the framework’s ability to measure drift across different parameter settings. All code and data used in our experimentation are publicly accessible online. 1

Providing recommendations that are both relevant and diverse is a key consideration of modern recommender systems. Optimizing both of these measures presents a fundamental trade-off, as higher diversity typically comes at the cost of relevance, resulting in lower user engagement. Existing recommendation algorithms try to resolve this trade-off by combining the two measures, relevance and diversity, into one aim and then seeking recommendations that optimize the combined objective, for a given number of items to recommend. Traditional approaches, however, do not consider the user interaction with the recommended items. In this paper, we put the user at the central stage, and build on the interplay between relevance, diversity, and user behavior. In contrast to applications where the goal is solely to maximize engagement, we focus on scenarios aiming at maximizing the total amount of knowledge encountered by the user. We use diversity as a surrogate of the amount of knowledge obtained by the user while interacting with the system, and we seek to maximize diversity. We propose a probabilistic user-behavior model in which users keep interacting with the recommender system as long as they receive relevant recommendations, but they may stop if the relevance of the recommended items drops. Thus, for a recommender system to achieve a high-diversity measure, it will need to produce recommendations that are both relevant and diverse. Finally, we propose a novel recommendation strategy that combines relevance and diversity by a copula function. We conduct an extensive evaluation of the proposed methodology over multiple datasets, and we show that our strategy outperforms several state-of-the-art competitors. Our implementation is publicly available at https://github.com/EricaCoppolillo/EXPLORE.

Popularity bias is the tendency of recommender systems to further suggest popular items while disregarding niche ones, hence giving no chance for items with low popularity to emerge. Although the literature is rich in debiasing techniques, it still lacks quality measures that effectively enable their analyses and comparisons. In this article, we first introduce a formal, data-driven, and parameter-free strategy for classifying items into low, medium, and high popularity categories. Then we introduce Balanced Quality Score (BQS) , a quality measure that rewards the debiasing techniques that successfully push a recommender system to suggest niche items, without losing points in its predictive capability in terms of global accuracy. We conduct tests of BQS on three distinct baseline collaborative filtering frameworks: one based on history-embedding and two on user/item-embedding modeling. These evaluations are performed on multiple benchmark datasets and against various state-of-the-art competitors, demonstrating the effectiveness of BQS.

Indexing multimedia content with rich and accurate metadata allows for improving the quality of the search engines’ results and boosting the recommender systems performances, which can benefit from this information to yield more effective recommendation lists. Therefore, the adoption of tools able to automatically label multimedia content with informative tags represents an important task for all the companies offering streaming entertainment services. However, domain experts generally perform the tagging process manually, making it time-consuming and error-prone. In the last few years, Machine Learning techniques have been proposed as a promising solution to automate this type of task, but the lack of clean and labeled training data hinders the learning of robust classification models. To cope with the issues described above, in this work, we devised a Deep Learning based solution for semi-automatic multi-label classification integrating post-hoc explanation techniques. Specifically, model explanation methods are exploited to assist the operator in the labeling process by facilitating an understanding of the model predictions. The proposed approach has been validated on a real dataset, and the experimental results demonstrate its effectiveness.