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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

19 October 2020

The latest content available from Springer
  • A serious game to extract Hofstede’s cultural dimensions at the individual level

    Abstract

    Cultural dimensions are an important aspect of a user model and useful for many applications such as adapting user interface, managing marketing campaigns, customer relationship management, and human resource management. Traditionally, these dimensions are measured through CVSCALE, which is a reliable and standard scale provided to measure Hofstede’s cultural dimensions at the individual level. The problems with the questionnaire-based data collection are low response rates, lack of willingness to complete, poor engagement of participants, and concern about the quality of data collected. To solve these problems, we present a serious video game called “Treasure Island” to measure the cultural dimensions of the player at the individual level (currently only for the Persian language). We have developed and validated a Persian-language version of CVSCALE applied to design the game. We developed the very first general game development process to build serious games for gathering the same data as a standard psychometric questionnaire. Treasure Island has been evaluated by statistical analysis of the data collected from a sample of 285 participants that have played the game and completed the questionnaire. The results indicate that Treasure Island is effective to measure the individual cultural dimensions. Moreover, the efficiency of the game has been tested in terms of task load imposed on the user during playing the game and completing the questionnaire. The results demonstrate that the game imposes less task load on the user, consequently, improves user satisfaction and engagement. Since individual cultural dimensions can be considered an important facet of a user model for certain applications, the proposed serious game can be applied for user modeling and personalization purposes.

  • The effects of controllability and explainability in a social recommender system

    Abstract

    In recent years, researchers in the field of recommender systems have explored a range of advanced interfaces to improve user interactions with recommender systems. Some of the major research ideas explored in this new area include the explainability and controllability of recommendations. Controllability enables end users to participate in the recommendation process by providing various kinds of input. Explainability focuses on making the recommendation process and the reasons behind specific recommendation more clear to the users. While each of these approaches contributes to making traditional “black-box” recommendation more attractive and acceptable to end users, little is known about how these approaches work together. In this paper, we investigate the effects of adding user control and visual explanations in a specific context of an interactive hybrid social recommender system. We present Relevance Tuner+, a hybrid recommender system that allows the users to control the fusion of multiple recommender sources while also offering explanations of both the fusion process and each of the source recommendations. We also report the results of a controlled study (N = 50) that explores the impact of controllability and explainability in this context.

  • Using scaffolding to formalize digital coach support for low-literate learners

    Abstract

    In this study, we attempt to specify the cognitive support behavior of a previously designed embodied conversational agent coach that provides learning support to low-literates. Three knowledge gaps are identified in the existing work: an incomplete specification of the behaviors that make up ‘support,’ an incomplete specification of how this support can be personalized, and unclear speech recognition rules. We use the socio-cognitive engineering method to update our foundation of knowledge with new online banking exercises, low-level scaffolding and user modeling theory, and speech recognition. We then refine the design of our coach agent by creating comprehensive cognitive support rules that adapt support based on learner needs (the ‘Generalized’ approach) and attune the coach’s support delay to user performance in previous exercises (the ‘Individualized’ approach). A prototype is evaluated in a 3-week within- and between-subjects experiment. Results show that the specified cognitive support is effective: Learners complete all exercises, interact meaningfully with the coach, and improve their online banking self-efficacy. Counter to hypotheses, the Individualized approach does not improve on the Generalized approach. Whether this indicates suboptimal operationalization or a deeper problem with the Individualized approach remains as future work.

  • Session-aware news recommendations using random walks on time-evolving heterogeneous information networks

    Abstract

    Traditional news media Web sites usually provide generic recommendations that are not personalized to the preferences of their users. Typically, news recommendation algorithms mainly rely on the long-term preferences of users and do not adjust their model to the continuous stream of short-lived incoming stories to capture short-term intentions revealed by users’ sessions. In this paper, we therefore study the problem of session-aware recommendations by running random walks on dynamic heterogeneous graphs. Concretely, we construct a heterogeneous information network consisting of users, news articles, news categories, locations and sessions. By using different (1) sliding time window sizes, (2) sub-graphs for model learning, (3) sequential article weighting strategies and (4) more diversified random walks, we perform recommendations in a second step. Our algorithm proposal is evaluated on three real-life data sets, and we demonstrate that our method outperforms state-of-the-art methods by delivering more accurate and diversified recommendations.

  • Using autoencoders for session-based job recommendations

    Abstract

    In this work, we address the problem of providing job recommendations in an online session setting, in which we do not have full user histories. We propose a recommendation approach, which uses different autoencoder architectures to encode sessions from the job domain. The inferred latent session representations are then used in a k-nearest neighbor manner to recommend jobs within a session. We evaluate our approach on three datasets, (1) a proprietary dataset we gathered from the Austrian student job portal Studo Jobs, (2) a dataset released by XING after the RecSys 2017 Challenge and (3) anonymized job applications released by CareerBuilder in 2012. Our results show that autoencoders provide relevant job recommendations as well as maintain a high coverage and, at the same time, can outperform state-of-the-art session-based recommendation techniques in terms of system-based and session-based novelty.