Nowadays, the profound digital transformation has upgraded the role of the computational system into an intelligent multidimensional communication medium that creates new opportunities, competencies, models and processes. The need for human-centered adaptation and personalization is even more recognizable since it can offer hybrid solutions that could adequately support the rising multi-purpose goals, needs, requirements, activities and interactions of users. The HAAPIE workshop1 embraces the essence of the “human-machine co-existence” and brings together researchers and practitioners from different disciplines to present and discuss a wide spectrum of related challenges, approaches and solutions. In this respect, the ninth edition of HAAPIE includes 4 long papers and 2 short papers.

With the global expansion of the Internet and the World Wide Web, users are becoming increasingly diverse, including their language proficiencies. In particular, there is now a significant number of polyglot Web users, i.e., users who are proficient in more than one language. However, even such users with potential access to a broad range of information from multiple languages often continue to suffer from unbalanced and fragmented news information, as traditional news access systems seldom allow users to simultaneously search for and/or compare news in different languages. To overcome language barriers, the majority of research has focused primarily on improving retrieval and translation accuracy, while paying comparably less attention to multilingual user interaction aspects. In particular, relatively little human-centered research has been conducted to better understand and support multilingual user abilities and preferences, and even less so regarding news search and different access modalities (such as desktop and mobile interfaces). The research presented in this article provides the first comparative analyses of polyglot users' preferences and behaviors with respect to different multilingual news search interfaces on both desktop and mobile platforms. Specifically, through a set of task-based user studies in laboratory experiments, the key contribution of this article is the presentation of the first human-centered studies in multilingual news search result interfaces, aiming to drive the development of human-centered multilingual news access systems for both desktop and mobile platforms. This contribution includes a detailed analysis of different interface design paradigms, as well as a series of implications for design.

Nowadays, the profound digital transformation has upgraded the role of the computational system into an intelligent multidimensional communication medium that creates new opportunities, competencies, models and processes. The need for human-centered adaptation and personalization is even more recognizable since it can offer hybrid solutions that could adequately support the rising multi-purpose goals, needs, requirements, activities and interactions of users. The HAAPIE workshop1 embraces the essence of the “human-machine co-existence” and brings together researchers and practitioners from different disciplines to present and discuss a wide spectrum of related challenges, approaches and solutions. In this respect, the seventh edition of HAAPIE includes 2 long papers and 5 short papers.

Visualisation is an important aspect to support human-ontology interaction, as visual cues amplify cognition and offload cognitive efforts to the human perceptual system.While significant research efforts have focused on visualisation layouts, adapting to the individual user has been largely overlooked in typical ontology visualisation systems.This provides an opportunity to potentially seek more personalised support in ontology visualisation.As such, this paper utilises a tumbling window analytical technique and demonstrates accurate predictions of a user's likelihood to succeed in a given task based on this person's latest gaze data during an interactive session.We show several trial scenarios where statistically significant accuracies are achieved for two commonly used ontology visualisations in the presence of mixed user backgrounds and task domains.In addition, depending on the gaze features that emphasise a user's search or processing activities, or cognitive workload, trial results show earlier predictions as well as higher accuracies can be achieved in some cases.Furthermore, an investigation of influential gaze features reveals a combination of gaze traits is often associated with higher user success.These findings motivate and highlight potentially ample opportunities to adapt to the individual user throughout various interactive stages in the realisation of adaptive ontology visualisation.

Nowadays, the profound digital transformation has upgraded the role of the computational system into an intelligent multidimensional communication medium that creates new opportunities, competencies, models and processes. The need for human-centered adaptation and personalization is even more recognizable since it can offer hybrid solutions that could adequately support the rising multi-purpose goals, needs, requirements, activities and interactions of users. The HAAPIE workshop1 embraces the essence of the “human-machine co-existence” and brings together researchers and practitioners from different disciplines to present and discuss a wide spectrum of related challenges, approaches and solutions. In this respect, the seventh edition of HAAPIE includes 5 long papers and 2 short papers.

Designing and developing innovative visualizations to assist humans in the process of generating and understanding complex semantic data has become an important element in supporting effective human-ontology interaction, as visual cues are likely to provide clarity, promote insight, and amplify cognition. While recent research has indicated potential benefits of applying novel adaptive technologies, typical ontology visualization techniques have traditionally followed a one-size-fits-all approach that often ignores an individual user's preferences, abilities, and visual needs. In an effort to realize adaptive ontology visualization, this paper presents a potential solution to predict a user's likely success and failure in real time, and prior to task completion, by applying established machine learning models on eye gaze generated during an interactive session. These predictions are envisioned to inform future adaptive ontology visualizations that could potentially adjust its visual cues or recommend alternative visualizations in real time to improve individual user success. This paper presents findings from a series of experiments to demonstrate the feasibility of gaze-based success and failure predictions in real time that can be achieved with a number of off-the-shelf classifiers without the need of expert configurations in the presence of mixed user backgrounds and task domains across two commonly used fundamental ontology visualization techniques.

User language proficiency has been shown to significantly affect search result language preferences, search behaviors, and even interface preferences. Consequently, search systems may consider adapting to a user's language proficiency when retrieving, composing, and presenting search results. In order to perform such adaptation, it is necessary to first get an estimate of a user's proficiency, ideally through simply observing their behaviors while searching. To this end, this paper investigates the extent to which a user's language proficiency can be inferred from their eye movements while they are evaluating search results. Classification results involving data from English-, Spanish-, and Chinese-speaking study participants show that such an inference is indeed possible, and with relatively high accuracies for all languages. It is also shown that feature sets involving statistics from an entire search result page, combined with gaze data from individual results, have the highest classification accuracies. Moreover, a user's Average Fixation Durations, Refixations, and Pupil Dilations are found to be the most significant features.

Many users of search systems are multilingual, that is, they are proficient in two or more languages. In order to better understand and support the language preferences and behaviors of such multilingual users, this paper presents a series of five large‐scale studies that specifically elicit language choices regarding search queries and result lists. Overall, the results from the studies indicate that users frequently make use of different languages (i.e., not just their primary language), especially when they are provided with choices (e.g., when provided with a secondary language query or result list choice). In particular, when presented with a mixed‐language list choice, participants choose this option to an almost equal extent compared to primary‐language‐only lists. Important factors leading to language choices are user‐, task‐ and system‐related, including proficiency, task topic, and result layout. Moreover, participants' subjective reasons for making particular choices indicate that their primary language is considered more comfortable, that the secondary language often has more relevant and trustworthy results, and that mixed‐language lists provide a better overview. These results provide crucial insights into multilingual user preferences and behaviors, and may help in the design of systems that can better support the querying and result exploration of multilingual users.

Nowadays, the profound digital transformation has upgraded the role of the computational system into an intelligent multidimensional communication medium that creates new opportunities, competencies, models and processes. The need for human-centered adaptation and personalization is even more recognizable since it can offer hybrid solutions that could adequately support the rising multi-purpose goals, needs, requirements, activities and interactions of users. The HAAPIE workshop1 embraces the essence of the “human-machine co-existence” and brings together researchers and practitioners from different disciplines to present and discuss a wide spectrum of related challenges, approaches and solutions. In this respect, the sixth edition of HAAPIE includes 3 long papers and 4 short papers.

Many users of search systems are multilingual, that is, they are proficient in two or more languages. In order to better understand and support the language preferences and behaviors of such multilingual users, this paper presents a series of five large-scale studies that specifically elicit language choices regarding search queries and result lists. Overall, the results from the studies indicate that users frequently make use of different languages (i.e., not just their primary language), especially when they are provided with choices (e.g., when provided with a secondary language query or result list choice). In particular, when presented with a mixed-language list choice, participants choose this option to an almost equal extent compared to primary-language-only lists. Important factors leading to language choices are user-, task- and system-related, including proficiency, task topic, and result layout. Moreover, participants' subjective reasons for making particular choices indicate that their primary language is considered more comfortable, that the secondary language often has more relevant and trustworthy results, and that mixed-language lists provide a better overview. These results provide crucial insights into multilingual user preferences and behaviors, and may help in the design of systems that can better support the querying and result exploration of multilingual users.

Information Visualization is a key technique to assist users in data analysis tasks, by creating visual representations of data to amplify human cognition. However, while human cognitive abilities and styles have been shown to differ significantly, Information Visualizations have traditionally been designed in a manner that does not consider such individual user differences. Recent research has started to address this issue, by identifying individual user characteristics that influence individual users' interactions with Information Visualizations, as well as developing novel Information Visualization systems that provide more personalized support. This paper presents a set of experiments aimed towards building such User-Adaptive Information Visualization systems, by studying the extent to which a user's cognitive style can be inferred from a user's interaction with an Information Visualization system. Results show that a user's eye gaze data can be used to infer a user's cognitive style during information visualization usage with up to 86% accuracy, and that the most informative features relate to a user's saccade angles and fixation durations.

With estimates suggesting that half of the world's population learns or speaks at least two languages, Web information access systems such as Web search engines need to cater for an increasing variety of individual language proficiencies and preferences. However, while significant advances have been made regarding the handling, retrieval, and automatic translation of multilingual information, there has been a relative lack of user-centered research aiming to support individual users' multilingual abilities. To address this research gap, this paper presents a series of user studies and experiments that aim to inform novel search solutions that specifically support multilingual users. In particular, the experiments presented in this paper examine the extent to which a system can predict, for a given query, what language(s) a multilingual user would prefer the search results to be in. Results from our studies show that such predictions can statistically significantly outperform a baseline model, and that users' languages and proficiencies, their current location, as well as the search topic domain and type all influence the prediction results.

Nowadays, the profound digital transformation has upgraded the role of the computational system into an intelligent multidimensional communication medium that creates new opportunities, competencies, models and processes. The need for human-centered adaptation and personalization is even more recognizable since it can offer hybrid solutions that could adequately support the rising multi-purpose goals, needs, requirements, activities and interactions of users. HAAPIE workshop embraces the essence of the "human-machine co-existence" and brings together researchers and practitioners from different disciplines to present and discuss a wide spectrum of related challenges, approaches and solutions. In this respect, the fifth edition of HAAPIE includes 5 long papers.

Information visualizations can be regarded as one of the most powerful cognitive tools to significantly amplify human cognition. However, traditional information visualization systems have been designed in a manner that does not consider individual user differences, even though human cognitive abilities and styles have been shown to differ significantly. In order to address this research gap, novel adaptive systems need to be developed that are able to (1) infer individual user characteristics and (2) provide an adaptation mechanism to personalize the system to the inferred characteristic. This paper presents a first step toward this goal by investigating the extent to which a user's cognitive style can be inferred from their behavior with an information visualization system. In particular, this paper presents a series of experiments that utilize features calculated from user eye gaze data in order to infer a user's cognitive style. Several different data and feature sets are presented, and results overall show that a user's eye gaze data can be used successfully to infer a user's cognitive style during information visualization usage.

Ontology visualization is an important component in the support of human-ontology interaction, as it amplifies cognition and offloads cognitive efforts to the human perceptual system. While a significant amount of research efforts has focused on designing and developing various visual layouts and improve performance of large-scale visualizations, the differences in user preferences and cognitive abilities have been largely overlooked. This provides an opportunity to investigate ways to potentially provide more personalized visual support in human-ontology interaction. To this end, this paper demonstrates successful predictions on an individual user's likelihood to succeed in a given task, based on this person's gaze data collected during interaction. Specifically, we show several statistically significant predictions against a baseline classifier when inferring users' success before a given task is actually completed. Moreover, we present results showing that accurate predictions of user success can be achieved early on during user interaction, such as after just a few minutes in some cases. These findings suggest there are ample opportunities throughout various stages of human-ontology interaction where the underlying visual system may adapt in real time to the user's visual needs to provide the most appropriate visualization with the overall goal of possibly increasing user success in a given task.

It is our great pleasure to welcome you to the UMAP 2019 LBR and Demo Track, in conjunction with the 27th Conference on User Modelling, Adaptation and Personalization, held in Larnaca, Cyprus on June 9-12th, 2019. This track encompasses two categories: (i) Demos, which showcase research prototypes and commercially available products of UMAP-based systems, (ii) Late-breaking Results (LBR), which contain original and unpublished accounts of innovative research ideas, preliminary results, industry showcases, and system prototypes, addressing both the theory and practice of UMAP. The submissions spanned a wide scope of topics, ranging from novel techniques for user and group modeling, to adaptation and personalization implementations across different application scenarios. We received 46 LBR and 4 Demo submissions. Each submission was carefully reviewed by members of the Demo and LBR program committee, which consisted of 89 members. Each submission was reviewed by at least 3 PC members. Out of this total of 50 submissions, 15 LBR and 3 Demos were deemed of good quality by the reviewers, and were consequently accepted (36% overall acceptance rate). They were presented in the UMAP poster sessions, which collectively showcased the wide spectrum of novel ideas and latest results in user modelling, adaptation and personalization.

Ontology visualization has played an important role in human data interaction by offering clarity and insight for complex structured datasets. Recent usability evaluations of ontology visualization techniques have added to our understanding of desired features when assisting users in the interactive process. However, user behavior data such as eye gaze and event logs have largely been used as indirect evidence to explain why a user may have carried out certain tasks in a controlled environment as opposed to direct input that informs the underlying visualization system. Although findings from usability studies have contributed to the refinement of ontology visualizations as a whole, the visualization techniques themselves remain a one-size-fits-all approach where all users are presented with the same visualizations and interactive features. By contrast, this paper investigates how user behavior data may offer real time indications as to how appropriate or effective a given visualization may be for a specific user at a moment in time, which in turn may inform the adaptation of the given visualization to the user on the fly. To this end, we apply established predictive modeling techniques in Machine Learning to predict user success using gaze data and event logs. We present a detailed analysis and demonstrate such predictions can be significantly better than a baseline classifier during visualization usage. These predictions can then be used to drive the adaptations of visual systems in providing ad hoc visualizations on a per user basis, which in turn may increase individual user success and performance.

Ontology visualization plays an important role in human data interaction by offering clarity and insight for complex structured datasets. Recent usability studies of ontology visualization techniques have added to our understanding of desired features when assisting users in the interactive process. However, user behavioral data such as eye gaze and event logs have largely been used as indirect evidence to explain why a user may have carried out certain tasks in a controlled environment, as opposed to direct input that informs the underlying visualization system. Although findings from usability studies have contributed to the refinement of ontology visualizations as a whole, the visualization techniques themselves remain a one-size-fits-all approach, where all users are presented with the same visualizations and interactive features. By contrast, this paper investigates the feasibility of using behavioral data, such as user gaze and event logs, as real-time indicators of how appropriate or effective a given visualization may be for a specific user at a moment in time, which in turn may be used to inform the adaptation of the visualization to the user on the fly. To this end, we apply established predictive modeling techniques in Machine Learning to predict user success using gaze data and event logs. We present a detailed analysis from a controlled experiment and demonstrate such predictions are not only feasible, but can also be significantly better than a baseline classifier during visualization usage. These predictions can then be used to drive the adaptations of visual systems in providing ad hoc visualizations on a per user basis, which in turn may increase individual user success and performance. Furthermore, we demonstrate the prediction performance using several different feature sets, and report on the results generated from several notable classifiers, where a decision tree-based learning model using a boosting algorithm produced the best overall results.