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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

23 February 2019

The latest content available from Springer
  • A cognition-centered personalization framework for cultural-heritage content

    Abstract

    The heterogeneity of the audience of cultural heritage institutions introduces numerous challenges to the delivery of the content. Considering that people differ in the way they perceive, process, and recall information and that their individual cognitive differences influence their experience, performance, and knowledge acquisition when performing cultural-heritage activities, the human-cognition factor should be considered as an important personalization factor within cultural-heritage contexts. To this end, we propose a cognition-centered personalization framework for delivering cultural-heritage activities, tailored to the users’ cognitive characteristics. The framework implements rule-based personalization algorithms that are based on cognition-centered user models that are created implicitly, transparently, and in run-time based on classifiers that correlate end-user cognitive characteristics with interaction and visual behavior patterns. For evaluating the proposed framework and improving the external validity of the experimental results, we conducted two eye-tracking between-subjects user-studies ( \(N=226\) ) covering two different cognitive styles (field dependence–independence and visualizer–verbalizer) and two different types of cultural activity (visual goal-oriented and visual exploratory). The results provide evidence about the applicability, effectiveness, and efficiency of the proposed framework and underpin the added value of adopting cognition-centered personalization frameworks within digitized cultural-heritage interaction contexts.

  • Personalized and content adaptive cultural heritage path recommendation: an application to the Gournia and Çatalhöyük archaeological sites

    Abstract

    Although abundant research work has been published in the area of path recommendation and its applications on travel and routing topics, scarce work has been reported on context-aware route recommendation systems aimed to stimulate optimal cultural heritage experiences. This paper tries to address this issue, by proposing a personalized and content adaptive cultural heritage path recommendation system, where location is modeled using mean-shift clustering trained with actual user movement patters. Additionally, topic modeling is incorporated to formalize the implicit cultural heritage content, while first order Markov models address the movement as a temporal transition aspect of the problem. The overall architecture is applied on data collected from actual visits to the archaeological sites of Gournia and Çatalhöyük and extensive analysis on visitor movement patterns follows, especially in comparison to the curated paths in the aforementioned sites. Finally, the offline evaluation results of the proposed recommendation scheme are encouraging, validating its efficiency and setting a positive paradigm for cultural heritage route recommendations.

  • Connectionist recommendation in the wild: on the utility and scrutability of neural networks for personalized course guidance

    Abstract

    The aggregate behaviors of users can collectively encode deep semantic information about the objects with which they interact. In this paper, we demonstrate novel ways in which the synthesis of these data can illuminate the terrain of users’ environment and support them in their decision making and wayfinding. A novel application of recurrent neural networks and skip-gram models, approaches popularized by their application to modeling language, are brought to bear on student university enrollment sequences to create vector representations of courses and map out traversals across them. We present demonstrations of how scrutability from these neural networks can be gained and how the combination of these techniques can be seen as an evolution of content tagging and a means for a recommender to balance user preferences inferred from data with those explicitly specified. From validation of the models to the development of a UI, we discuss additional requisite functionality informed by the results of a usability study leading to the ultimate deployment of the system at a university.

  • Addressing the user cold start with cross-domain collaborative filtering: exploiting item metadata in matrix factorization

    Abstract

    Providing relevant personalized recommendations for new users is one of the major challenges in recommender systems. This problem, known as the user cold start has been approached from different perspectives. In particular, cross-domain recommendation methods exploit data from source domains to address the lack of user preferences in a target domain. Most of the cross-domain approaches proposed so far follow the paradigm of collaborative filtering, and avoid analyzing the contents of the items, which are usually highly heterogeneous in the cross-domain setting. Content-based filtering, however, has been successfully applied in domains where item content and metadata play a key role. Such domains are not limited to scenarios where items do have text contents (e.g., books, news articles, scientific papers, and web pages), and where text mining and information retrieval techniques are often used. Potential application domains include those where items have associated metadata, e.g., genres, directors and actors for movies, and music styles, composers and themes for songs. With the advent of the Semantic Web, and its reference implementation Linked Data, a plethora of structured, interlinked metadata is available on the Web. These metadata represent a potential source of information to be exploited by content-based and hybrid filtering approaches. Motivated by the use of Linked Data for recommendation purposes, in this paper we present and evaluate a number of matrix factorization models for cross-domain collaborative filtering that leverage metadata as a bridge between items liked by users in different domains. We show that in case the underlying knowledge graph connects items from different domains and then in situations that benefit from cross-domain information, our models can provide better recommendations to new users while keeping a good trade-off between recommendation accuracy and diversity.

  • Acknowledgment to reviewers