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.

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Latest Results for User Modeling and User-Adapted Interaction

12 November 2018

The latest content available from Springer
  • Identifying factors that influence the acceptability of smart devices: implications for recommendations


    This paper presents results from a web-based study that investigates users’ attitudes toward smart devices, focusing on acceptability. Specifically, we conducted a survey that elicits users’ ratings of devices in isolation and devices in the context of tasks potentially performed by these devices. Our study led to insights about users’ attitudes towards devices in isolation and in the context of tasks, and about the influence of demographic factors and factors pertaining to technical expertise and experience with devices on users’ attitudes. The insights about users’ attitudes provided the basis for two recommendation approaches based on principal components analysis (PCA) that alleviate the new-user and new-item problems: (1) employing latent features identified by PCA to predict ratings given by existing users to new devices, and by new users to existing devices; and (2) identifying a relatively small set of key questions on the basis of PCs, whose answers account to a large extent for new users’ ratings of devices in isolation and in the context of tasks. Our results show that taking into account latent features of devices, and asking a relatively small number of key questions about devices in the context of tasks, lead to rating predictions that are significantly more accurate than global and demographic predictions, and substantially reduce prediction error, eventually matching the performance of strong baselines.

  • Evaluation of session-based recommendation algorithms


    Recommender systems help users find relevant items of interest, for example on e-commerce or media streaming sites. Most academic research is concerned with approaches that personalize the recommendations according to long-term user profiles. In many real-world applications, however, such long-term profiles often do not exist and recommendations therefore have to be made solely based on the observed behavior of a user during an ongoing session. Given the high practical relevance of the problem, an increased interest in this problem can be observed in recent years, leading to a number of proposals for session-based recommendation algorithms that typically aim to predict the user’s immediate next actions. In this work, we present the results of an in-depth performance comparison of a number of such algorithms, using a variety of datasets and evaluation measures. Our comparison includes the most recent approaches based on recurrent neural networks like gru4rec, factorized Markov model approaches such as fism or fossil, as well as simpler methods based, e.g., on nearest neighbor schemes. Our experiments reveal that algorithms of this latter class, despite their sometimes almost trivial nature, often perform equally well or significantly better than today’s more complex approaches based on deep neural networks. Our results therefore suggest that there is substantial room for improvement regarding the development of more sophisticated session-based recommendation algorithms.

  • James Chen Annual Award for Best Journal Article
  • Inferring user interests in microblogging social networks: a survey


    With the growing popularity of microblogging services such as Twitter in recent years, an increasing number of users are using these services in their daily lives. The huge volume of information generated by users raises new opportunities in various applications and areas. Inferring user interests plays a significant role in providing personalized recommendations on microblogging services, and also on third-party applications providing social logins via these services, especially in cold-start situations. In this survey, we review user modeling strategies with respect to inferring user interests from previous studies. To this end, we focus on four dimensions of inferring user interest profiles: (1) data collection, (2) representation of user interest profiles, (3) construction and enhancement of user interest profiles, and (4) the evaluation of the constructed profiles. Through this survey, we aim to provide an overview of state-of-the-art user modeling strategies for inferring user interest profiles on microblogging social networks with respect to the four dimensions. For each dimension, we review and summarize previous studies based on specified criteria. Finally, we discuss some challenges and opportunities for future work in this research domain.

  • The details matter: methodological nuances in the evaluation of student models


    The core of student modeling research is about capturing the complex learning processes into an abstract mathematical model. The student modeling research, however, also involves important methodological aspects. Some of these aspects may seem like technical details not worth significant attention. However, the details matter. We discuss three important methodological issues in student modeling: the impact of data collection, the splitting of data into a training set and a test set, and the details concerning averaging in the computation of predictive accuracy metrics. We explicitly identify decisions involved in these steps, illustrate how these decisions can influence results of experiments, and discuss consequences for future research in student modeling.