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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.

UMUAI has been published since 1991 by Kluwer Academic Publishers (now merged with Springer Verlag).

UMUAI homepage with description of the scope of the journal and instructions for authors.

Springer UMUAI page with online access to the papers.

Latest Results for User Modeling and User-Adapted Interaction

09 December 2019

The latest content available from Springer
  • An investigation on the user interaction modes of conversational recommender systems for the music domain

    Abstract

    Conversational Recommender Systems (CoRSs) implement a paradigm that allows users to interact in natural language with the system for defining their preferences and discovering items that best fit their needs. CoRSs can be straightforwardly implemented as chatbots that, nowadays, are becoming more and more popular for several applications, such as customer care, health care, and medical diagnoses. Chatbots implement an interaction based on natural language, buttons, or both. The implementation of a chatbot is a challenging task since it requires knowledge about natural language processing and human–computer interaction. A CoRS might be particularly useful in the music domain since music is generally enjoyed in contexts when a standard interface cannot be exploited (driving, doing homeworks, running). However, there is no work in the literature that analytically compares different interaction modes for a conversational music recommender system. In this paper, we focus on the design and implementation of a CoRS for the music domain. Our CoRS consists of different components. The system implements content-based recommendation, critiquing and adaptive strategies, as well as explanation facilities. The main innovative contribution is that the user can interact through different interaction modes: natural language, buttons, and mixed. Due to the lack of available datasets for testing CoRSs, we carried out an in vivo experimental evaluation with the goal of investigating the impact of the different interaction modes on the recommendation accuracy and on the cost of interaction for the final user. The experiment involved 110 people, and 54 completed the whole process. The analysis of the results shows that the best interaction mode is based on a mixed strategy that combines buttons and natural language. In addition, the results allow to clearly understand which are the steps in the dialog that are particularly strenuous for the user.

  • Effects of personal characteristics in control-oriented user interfaces for music recommender systems

    Abstract

    Music recommender systems typically offer a “one-size-fits-all” approach with the same user controls and visualizations for all users. However, the effectiveness of interactive interfaces for music recommender systems is likely to be affected by individual differences. In this paper, we first conduct a comprehensive literature review of interactive interfaces in recommender systems to motivate the need for personalized interaction with music recommender systems, and two personal characteristics,  visual memory and musical sophistication. More specifically, we studied the influence of these characteristics on the design of (a) visualizations for enhancing recommendation diversity and (b) the optimal level of user controls while minimizing cognitive load. The results of three experiments show a benefit for personalizing both visualization and control elements to musical sophistication. We found that (1) musical sophistication influenced the acceptance of recommendations for user controls. (2) musical sophistication also influenced recommendation acceptance, and perceived diversity for visualizations and the UI combining user controls and visualizations. However, musical sophistication only strengthens the impact of UI on perceived diversity (moderation effect) when studying the combined effect of controls and visualizations. These results allow us to extend the model for personalization in music recommender systems by providing guidelines for interactive visualization design for music recommender systems, with regard to both visualizations and user control.

  • Acknowledgement to reviewers
  • Conflict resolution in group decision making: insights from a simulation study

    Abstract

    An individual’s conflict resolution styles can have a large impact on the decision making process of a group. This impact is affected by a variety of factors, such as the group size, the similarity of the group members, and the type of support offered by the recommender system, if the group is using one. Measuring the effect of these factors goes beyond the capability of a live user study. In this article we show that simulation-based experiments can be effectively exploited to analyse the effect of the group members’ conflict resolution styles and to help researchers to formulate additional research hypotheses, which could be individually tested in ad hoc user studies. We therefore propose a group discussion procedure that simulates users’ actions while trying to make a group decision. The simulated users adopt alternative conflict resolution styles derived from the Thomas–Kilmann Conflict Model. The simulation procedure is informed by the analysis of real users’ interaction logs with a group discussion support system. Our experiments are conducted on scenarios characterized by four group factors, namely, conflict resolution style, inner-group similarity, interaction length and group size. We demonstrate the effect of these factors on the recommendation quality. This is measured by the loss in the utility obtained by an individual when choosing the recommended group choice rather than his/her individual best choice. We also measure the difference between the highest and lowest utility that the group members obtain, in order to understand the fairness of the group recommendation identified by the system. The experimental results show (among other findings) that if group members have similar tastes then groups composed of users with the competing conflict resolution style obtain the largest utility loss, compared to groups whose members adopt the cooperative styles (accommodating and collaborating), and yet, whatever their conflict resolution styles, there is no distinct difference in their utility for the group choice (they are treated equally). Conversely, when group members have diverse preferences, the average utility loss of competing members is still the largest, but the differences in their utility is the lowest (they all get a similar but lower utility). Some of the findings of our simulation experiments also match observations made in real group discussions and they pave the way for new user studies aimed at further supporting the reported findings.

  • Equipping the ACT-R cognitive architecture with a temporal ratio model of memory and using it in a new intelligent adaptive interface

    Abstract

    ACT-R, as a useful and well-known cognitive architecture, is a theory for simulating and understanding human cognition. However, the standard version of this architecture uses a deprecated forgetting model. So, we equipped it with a temporal ratio model of memory that has been named as SIMPLE (Scale-Independent Memory, Perception, and Learning). On the other hand, one of the usages of cognitive architectures is to model the user in an Intelligent Adaptive Interface (IAI) implementation. Thus, our motivation for this effort is to use this equipped ACT-R in an IAI to deliver the right information at the right time to users based on their cognitive needs. So, to test our proposed equipped ACT-R, we designed and implemented a new IAI to control a swarm of Unmanned Aerial Vehicles (UAVs). This IAI uses the equipped ACT-R for user cognitive modeling, to deliver the right information to the users based on their forgetting model. Thus, our contributions are: equipping the ACT-R cognitive architecture with the SIMPLE memory model and using this equipped version of ACT-R for user modeling in a new IAI to control a group of UAVs. Simulation results, which have been obtained using different subjective and objective measures, show that we significantly improved situation awareness of the users using the IAI empowered by our equipped ACT-R.