User Modeling and User-Adapted Interaction (UMUAI) provides an interdisciplinary forum for the dissemination of new research results on interactive computer systems that can be adapted or adapt themselves to their current users, and on the role of user models in the adaptation process.

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Latest Results for User Modeling and User-Adapted Interaction

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02 March 2024

The latest content available from Springer
  • Evaluating explainable social choice-based aggregation strategies for group recommendation


    Social choice aggregation strategies have been proposed as an explainable way to generate recommendations to groups of users. However, it is not trivial to determine the best strategy to apply for a specific group. Previous work highlighted that the performance of a group recommender system is affected by the internal diversity of the group members’ preferences. However, few of them have empirically evaluated how the specific distribution of preferences in a group determines which strategy is the most effective. Furthermore, only a few studies evaluated the impact of providing explanations for the recommendations generated with social choice aggregation strategies, by evaluating explanations and aggregation strategies in a coupled way. To fill these gaps, we present two user studies (N=399 and N=288) examining the effectiveness of social choice aggregation strategies in terms of users’ fairness perception, consensus perception, and satisfaction. We study the impact of the level of (dis-)agreement within the group on the performance of these strategies. Furthermore, we investigate the added value of textual explanations of the underlying social choice aggregation strategy used to generate the recommendation. The results of both user studies show no benefits in using social choice-based explanations for group recommendations. However, we find significant differences in the effectiveness of the social choice-based aggregation strategies in both studies. Furthermore, the specific group configuration (i.e., various scenarios of internal diversity) seems to determine the most effective aggregation strategy. These results provide useful insights on how to select the appropriate aggregation strategy for a specific group based on the level of (dis-)agreement within the group members’ preferences.

  • Persuasion-enhanced computational argumentative reasoning through argumentation-based persuasive frameworks


    One of the greatest challenges of computational argumentation research consists of creating persuasive strategies that can effectively influence the behaviour of a human user. From the human perspective, argumentation represents one of the most effective ways to reason and to persuade other parties. Furthermore, it is very common that humans adapt their discourse depending on the audience in order to be more persuasive. Thus, it is of utmost importance to take into account user modelling features for personalising the interactions with human users. Through computational argumentation, we can not only devise the optimal solution, but also provide the rationale for it. However, synergies between computational argumentative reasoning and computational persuasion have not been researched in depth. In this paper, we propose a new formal framework aimed at improving the persuasiveness of arguments resulting from the computational argumentative reasoning process. For that purpose, our approach relies on an underlying abstract argumentation framework to implement this reasoning and extends it with persuasive features. Thus, we combine a set of user modelling and linguistic features through the use of a persuasive function in order to instantiate abstract arguments following a user-specific persuasive policy. From the results observed in our experiments, we can conclude that the framework proposed in this work improves the persuasiveness of argument-based computational systems. Furthermore, we have also been able to determine that human users place a high level of trust in decision support systems when they are persuaded using arguments and when the reasons behind the suggestion to modify their behaviour are provided.

  • Connecting physical activity with context and motivation: a user study to define variables to integrate into mobile health recommenders


    In this paper, we aim to improve existing health recommender systems by defining relevant contextual and motivational variables to recommend physical activities and collect appreciation feedback. Current health recommenders do not sufficiently include users’ context and motivational theory when personalizing health suggestions. To bridge these gaps, we conducted a 21-day longitudinal user study with 36 participants using our Android app with collected sensor data and Ecological Momentary Assessments to collect daily activities, mood, and motivation. This study resulted in a dataset of 724 activities. Two approaches to determine feature relevance were followed: variable importances analysis on 40 input variables, and statistical analysis of mean differences in outcome variables across contexts. Our findings suggest recommending activity duration, intensity, location, and type by incorporating: company, situation (e.g., free time or work), happiness, calmness, energy level, physical complaints, and motivation. As such, we propose opportunities for future health recommenders to integrate these data with contextual pre-filtering techniques, extended with our suggestions for automatically collected weather, location types, step count, and time. We also propose to use mood and motivation as appreciation feedback to focus on user well-being and boost motivation.

  • Choice models and recommender systems effects on users’ choices


    Nowadays, the users of a web platform, such as a video-on-demand service or an eCommerce site, are routinely using the platform’s recommender system (RS) when choosing which item to consume or buy (e.g. movies or books). It is therefore important to understand how the exposure to recommendations can influence the users’ choices, particularly the quality and distribution of the chosen items. However, users, even in the presence of the same RS, may show diverse and even atypical choice behaviours, which are independent of the RS; they may have a preference for choosing more popular or recent items. The effect of these behaviours on the collective evolution of the choices and the performance of the RS is not well-understood yet. In fact, in previous analyses, the users were supposed to only choose among the top recommendations, without any further discrimination. Hence, we first perform a correlation analysis, in some choice data sets, revealing that three kinds of choice behaviours, namely the tendency to choose popular, recent, and highly rated items, are actually observable in large percentages of the users. Then, we investigate how these choice behaviours, implemented as algorithmic choice models (Popularity-CM, Age-CM and Rating-CM), can influence the overall choice distribution and performance of the RS. With the aim of understanding such relationships and consequences, we have designed a simulation framework where the considered choice models (CMs) are adopted to simulate users’ choices when they are exposed to recommendations from alternative RSs. We found that (1) the choices’ distribution of a user population is significantly influenced not only by the RS, but also by the prevalent choice model of the population, (2) RS have some effects on the choices that are independent of the adoption of the CM, and (3) some important effects of the RS on users’ choice distribution depend also on the choice model that the users adopt. The study contributes to the start of a new line of research where the impact of recommendation technologies can be studied with respect to alternative decision-making approaches, which are actually followed by real users. Additionally, the simulation approach can help other researchers and practitioners to investigate the effect of deploying an RS when a certain CM is identified in a population of users.

  • Fairness in recommender systems: research landscape and future directions


    Recommender systems can strongly influence which information we see online, e.g., on social media, and thus impact our beliefs, decisions, and actions. At the same time, these systems can create substantial business value for different stakeholders. Given the growing potential impact of such AI-based systems on individuals, organizations, and society, questions of fairness have gained increased attention in recent years. However, research on fairness in recommender systems is still a developing area. In this survey, we first review the fundamental concepts and notions of fairness that were put forward in the area in the recent past. Afterward, through a review of more than 160 scholarly publications, we present an overview of how research in this field is currently operationalized, e.g., in terms of general research methodology, fairness measures, and algorithmic approaches. Overall, our analysis of recent works points to certain research gaps. In particular, we find that in many research works in computer science, very abstract problem operationalizations are prevalent and questions of the underlying normative claims and what represents a fair recommendation in the context of a given application are often not discussed in depth. These observations call for more interdisciplinary research to address fairness in recommendation in a more comprehensive and impactful manner.