Departamento de Informática
URI permanente para esta comunidadhttps://hdl.handle.net/10953/35
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Ítem Group recommender systems for tourism: current state and future directions(2025) Yera, Raciel; Carballo-Cruz, Edianny; Maroto-Martos, Juan CarlosRecommender 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.Ítem With Friends Like These, Who Needs Explanations? Evaluating User Understanding of Group Recommendations(ACM, 2025-06) Waterschoot, Cedric; Yera, Raciel; Tintarev, Nava; Barile, FrancescoGroup 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.