Departamento de Informática

URI permanente para esta comunidadhttps://hdl.handle.net/10953/35

En esta Comunidad se recogen los documentos generados por el Departamento de Informática y que cumplen los requisitos de Copyright para su difusión en acceso abierto.

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An Ensemble for Automatic Time Series Forecasting With K-Nearest Neighbors
(IEEE, 2025-01-02) Frías, María Pilar; Martínez-del-Río, Francisco
In this paper a novel approach for automatically configuring a k-nearest neighbors regressor for univariate time series forecasting is presented. The approach uses an ensemble consisting of several k-nearest neighbors models with different configurations for their hyperparameters and model selection choices. One advantage of this scheme is that the uncertainty associated with choosing a wrong configuration for the model is reduced. This approach is compared with the classical way of selecting a configuration by doing a grid search among several configurations of hyperparameters and model selection choices and choosing the one that performs best on a validation set. The experimental results, using datasets from time series forecasting competitions, show that, in line with previous works, the use of an ensemble produces a robust model, outperforming the approach that uses a grid search for obtaining the best configuration on a validation set and almost any specific configuration. The forecast accuracy of the ensemble is similar to state-of-theart models. Furthermore, this paper also tests the effectiveness of some recent approaches for dealing with trending time series when using the k-nearest neighbors algorithm.
Group recommender systems for tourism: current state and future directions
(2025) Yera, Raciel; Carballo-Cruz, Edianny; Maroto-Martos, Juan Carlos
Recommender systems, as sucessful personalization tools, are focused on helping potential clients to obtain the information that best corresponds to their needs and preferences, in a search space overloaded with options. Properly, group recommender systems (GRSs) focus on suggesting certain types of item (products or services) that tend to be consumed in groups. This paper analyzes the use of GRSs in the tourism domain. It considers the most representative works in this area, highlighting the type of study and evaluation, the application scenario, its purpose, and the methodology used for the generation of recommendations. With this goal, the PRISMA methodology is used, being 1115 works initially identified in the Web of Science and Scopus, which are reduced into a set of 79 articles after the application of several inclusion and exclusion criteria. A comprehensive study of such works identifies three main working domains: 1) GRSs for restaurants, 2) GRSs for itineraries and points of interest (POIs), and 3) GRSs for destination and travel. An overview of the main methodologies and evaluation approaches in these works is also discussed. The analysis performed also identifies relevant research lines for future work.
Time Series Forecasting by Generalized Regression Neural Networks Trained With Multiple Series
(IEEE, 2022-01-04) Martínez-del-Río, Francisco; Frías, María Pilar; Pérez-Godoy, María Dolores; Rivera-Rivas, Antonio Jesús
Time series forecasting plays a key role in many fields such as business, energy or environment. Traditionally, statistical or machine learning models for time series forecasting are trained with the historical values of the series to be forecast. Unfortunately, some time series are too short to suitably train a model. Motivated by this fact, this paper explores the use of data available in a pool or collection of time series to train a model that predicts an individual series. Concretely, we train a generalized regression neural network with the examples drawn from the historical values of a pool of series and then use the model to forecast individual series. In this sense several approaches are proposed, including to draw the examples from a pool of series related to the series to be forecast or the training of several models with mutually exclusive series and the combination of their forecasts. Experimental results in terms of forecasting accuracy using generalized regression neural networks are promising. Furthermore, the proposed approaches allow to forecast series that are too short to build a traditional generalized regression neural network model.
With Friends Like These, Who Needs Explanations? Evaluating User Understanding of Group Recommendations
(ACM, 2025-06) Waterschoot, Cedric; Yera, Raciel; Tintarev, Nava; Barile, Francesco
Group Recommender Systems (GRS) employing social choice-based aggregation strategies have previously been explored in terms of perceived consensus, fairness, and satisfaction. At the same time, the impact of textual explanations has been examined, but the results suggest a low effectiveness of these explanations. However, user understanding remains fairly unexplored, even if it can contribute positively to transparent GRS. This is particularly interesting to study in more complex or potentially unfair scenarios when user preferences diverge, such as in a minority scenario (where group members have similar preferences, except for a single member in a minority position). In this paper, we analyzed the impact of different types of explanations on user understanding of group recommendations. We present a randomized controlled trial (𝑛 = 271) using two between-subject factors: (i) the aggregation strategy (additive, least misery, and approval voting), and (ii) the modality of explanation (no explanation, textual explanation, or multimodal explanation). We measured both subjective (self-perceived by the user) and objective understanding (performance on model simulation, counterfactuals and error detection). In line with recent findings on explanations for machine learning models, our results indicate that more detailed explanations, whether textual or multimodal, did not increase subjective or objective understanding. However, we did find a significant effect of aggregation strategies on both subjective and objective understanding. These results imply that when constructing GRS, practitioners need to consider that the choice of aggregation strategy can influence the understanding of users. Post-hoc analysis also suggests that there is value in analyzing performance on different tasks, rather than through a single aggregated metric of understanding.

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