ACM UMAP 2017 is the 25th conference on User Modeling, on Adaptive Hypermedia, or on both together (since 2009). The research has actually been going on for more than 25 years as initially there was a conference only every two years. This keynote offers both reflection on the past and outlook into the future, with the burning question: What makes us UMAP? We perform research on modeling users (individuals as well as groups), not just for fun but to use these models for recommendations and for adaptation. That's not unique to us. In recommender systems analyzing user behavior is needed in order to give better and better recommendations, and likewise an area like educational data mining analyzes how learners study in order to best guide them to new learning material or followup courses. With analysis of social networks and website adaptation we step into the same research area that is covered by the hypertext community. If all of this is "us" but "not just us", where is our identity?

Computing containment relations between massive collections of sets is a fundamental operation in data management, for example in graph analytics and data mining applications. Motivated by recent hardware trends, in this paper we present two novel solutions for computing set-containment joins over massive sets: the Patricia Trie-based Signature Join (PTSJ) and PRETTI+, a Patricia trie enhanced extension of the state-of-the-art PRETTI join. The compact trie structure not only enables efficient use of main-memory, but also significantly boosts the performance of both approaches. By carefully analyzing the algorithms and conducting extensive experiments with various synthetic and real-world datasets, we show that, in many practical cases, our algorithms are an order of magnitude faster than the state-of-the-art.

In this paper, we present, to our knowledge, the first known I/O efficient solutions for computing the k-bisimulation partition of a massive directed graph, and performing maintenance of such a partition upon updates to the underlying graph. Ubiquitous in the theory and application of graph data, bisimulation is a robust notion of node equivalence which intuitively groups together nodes in a graph which share fundamental structural features. k-bisimulation is the standard variant of bisimulation where the topological features of nodes are only considered within a local neighborhood of radius k > 0.

Design of feedback is a critical issue of online assessment development within Web-based Learning Systems (WBLSs). This paper examines the potential possibilities of tailoring the feedback that is presented to a student as a result of his/her preferences and responses to questions of an online test with respect to the individual learning styles (LS). The paper briefly reviews the main types of feedback that can be presented during online assessment and discusses the challenges in authoring and tailoring of feedback in WBLSs. We report the results of some recent experiments organized as online assessment of students through multiple-choice quizzes in which students were able to request different kinds of feedback for the answered questions. The experimental results have confirmed that LS have a significant influence on (1) the feedback preferences (with regard to response certitude and correctness) of students and (2) the effectiveness of elaborated feedback (EF), i.e. improving studentspsila performance during the test.

There is a genuine demand for personalization and guidance in learning systems, as well as in general commercial learning systems for the WWW, and further, for the new, emerging Semantic Web. Some isolated attempts towards adaptation are being made without fully responding to this demand. However, systematic frameworks for adaptive content and behavior description are already available in the research on Adaptive Hypermedia (AH). Here we present a novel application of an Adaptive Hypermedia Authoring framework and its principles towards extending an existing SME authoring environment for e-learning, as well as some initial evaluation results.

In order to efficiently manage information on the Web it becomes crucial to provide personalization not only within single systems, but across various Web applications. Interoperability of user models, on both syntactic and semantic level, emerges as a vital issue in order to achieve seamless personalization across different user-adaptive systems. In this paper we discuss the architecture of a broker-based discovery service for user models (BD-SUM), which allows applications to discover and invoke semantically described user models, in a multi-application context.

The goal of the CHIP (Cultural Heritage Information Personalization) project is to provide personalized access to combined cultural heritage content. The driving case is given by the Rijksmuseum content presented on the museum web site and visitor guides. The CHIP project aims to extend and integrate existing technologies for semantic browsing and search (e.g. Topia1[1], Noadster[2], OntoAIMS), ontology-based user modeling (e.g. OWL-OLM2[3]), adaptation strategies (e.g. AHA! 3[4]) and presentation of structured information (e.g. Hera4[5]) in order to achieve its goals. We use as a starting point the Topia demonstrator, which provides search and browsing interface to 1250 semantically described Rijksmuseum artifacts. We extend further this research by involving semantic-aware adaptation strategies and semantic based reasoning user model elicitation to allow for effective personalization of the content navigation and presentation on various devices (e. g. web application, PDA, etc.). In this we integrate artifacts and background information that span over several collections and sources. Adaptive clustering mechanisms show the relations between art objects and links to background information, based on their metadata. Further, hypermedia formats support multi-branched story lines connecting art objects in “ precooked” or automatically generated ways.

Hypertext is being used more and more often for on-line course texts. However, the navigational freedom offered by a rich link structure is a burden for students who need guidance throughout the learning process. This paper presents a framework for adaptive link structures. By enabling links when a student is ready to read the pages these links lead to, and disabling links to pages that are no longer needed, the student can be assured that links always lead to interesting new information she is ready to read. This framework is illustrated by means of the courseware for an on-line course on ‘Hypermedia structures and systems’, developed at the Eindhoven University of Technology.