Similarity-based analysis is a powerful and intuitive tool for exploring large data sets, for instance, for revealing patterns by grouping items by similarity or for recommending items based on selected samples. However, similarity is an abstract and subjective property which makes it hard to evaluate by a purely computational approach. Furthermore, there are usually several possible computational models that could be applied to the data, each with its own strengths and weaknesses. With this in mind, we aim to extend the research frontier regarding what impact the choice of a computational model may have on the results. In this paper, we target the scope of embedding-based similarity calculations on text documents and seek to answer the research question: "How can a better understanding of the continuous similarity distribution captured by different models lead to better similarity calculations on document sets?". We propose a new and generic methodology based on similarity network comparison, and based on this approach, we have developed a computational pipeline together with a prototype visual analytics tool that allows the user to easily assess the level of model agreement/disagreement. To demonstrate the potential of our method, as well as showing its application to real world scenarios, we apply it in an experimental setup using three state-of-the-art text embedding models and three different text corpora. In view of the surprisingly low level of model agreement regarding the data, we also discuss strategies for handling model disagreement.

The sensemaking process of large sets of text documents is highly challenging for tasks such as obtaining a comprehensive overview or keeping up with the most important trends and topics. Even though several established methods for condensation and summarization of large text corpora exist, many of them lack the ability to account for difference in prevalence between identified topics, which in turn impedes quantitative analysis. In this paper, we therefore propose a novel prevalence-aware method for topic extraction, and show how it can be used to obtain important insights from two text corpora with very different content. We also implemented a prototype visual analytics tool which guides the user in the search for relevant insights and promotes trust in the yielded results. We have verified our application by a user study, as well as by a validation run on a data set with previously known topic structure. The results clearly show that our approach is suitable for text mining, that is can be used by non-experts, and that it offers features which makes it an interesting candidate for use in several different analyze scenarios.

This article presents a systematic literature review on hybrid recommendation systems (HRS) in the e-commerce sector, a field characterized by constant innovation and rapid growth. As the complexity and volume of digital data increases, recommendation systems have become essential in guiding customers to services or products that align with their interests. However, the effectiveness of single-architecture recommendation algorithms is often limited by issues such as data sparsity, challenges in understanding user needs, and the cold start problem. Hybridization, which combines multiple algorithms in different methods, has emerged as a dominant solution to these limitations. This approach is utilized in various domains, including e-commerce, where it significantly improves user experience and sales. To capture the recent trends and advancements in HRS within e-commerce over the past six years, we review the state-of-the-art overview of HRS within e-commerce. This review meticulously evaluates existing research, addressing primary inquiries and presenting findings that contribute to evidence-based decision-making, understanding research gaps, and maintaining transparency. The review begins by establishing fundamental concepts, followed by detailed methodologies, findings from addressing the research questions, and exploration of critical aspects of HRS. In summarizing and incorporating existing research, this paper offers valuable insights for researchers and outlines potential avenues for future research, ultimately providing a comprehensive overview of the current state and prospects of HRS in e-commerce.

Embeddings are powerful tools for transforming complex and unstructured data into numeric formats suitable for computational analysis tasks. In this paper, we extend our previous work on using multiple embeddings for text similarity calculations to the field of networks. The embedding ensemble approach improves network reconstruction performance compared to single-embedding strategies. Our visual analytics methodology is successful in handling both text and network data, which demonstrates its generalizability beyond its originally presented scope.

Comparing text documents is an essential task for a variety of applications within diverse research fields, and several different methods have been developed for this. However, calculating text similarity is an ambiguous and context-dependent task, so many open challenges still exist. In this paper, we present a novel method for text similarity calculations based on the combination of embedding technology and ensemble methods. By using several embeddings, instead of only one, we show that it is possible to achieve higher quality, which in turn is a key factor for developing high-performing applications for text similarity exploitation. We also provide a prototype visual analytics tool which helps the analyst to find optimal performing ensembles and gain insights to the inner workings of the similarity calculations. Furthermore, we discuss the generalizability of our key ideas to fields beyond the scope of text analysis.

BACKGROUND: Parkinson disease (PD) is a chronic degenerative disorder that causes progressive neurological deterioration with profound effects on the affected individual's quality of life. Therefore, there is an urgent need to improve patient empowerment and clinical decision support in PD care. Home-based disease monitoring is an emerging information technology with the potential to transform the care of patients with chronic illnesses. Its acceptance and role in PD care need to be elucidated both among patients and caregivers.

OBJECTIVE: Our main objective was to develop a novel home-based monitoring system (named EMPARK) with patient and clinician interface to improve patient empowerment and clinical care in PD.

METHODS: We used elements of design science research and user-centered design for requirement elicitation and subsequent information and communications technology (ICT) development. Functionalities of the interfaces were the subject of user-centric multistep evaluation complemented by semantic analysis of the recorded end-user reactions. The ICT structure of EMPARK was evaluated using the ICT for patient empowerment model.

RESULTS: Software and hardware system architecture for the collection and calculation of relevant parameters of disease management via home monitoring were established. Here, we describe the patient interface and the functional characteristics and evaluation of a novel clinician interface. In accordance with our previous findings with regard to the patient interface, our current results indicate an overall high utility and user acceptance of the clinician interface. Special characteristics of EMPARK in key areas of interest emerged from end-user evaluations, with clear potential for future system development and deployment in daily clinical practice. Evaluation through the principles of ICT for patient empowerment model, along with prior findings from patient interface evaluation, suggests that EMPARK has the potential to empower patients with PD.

CONCLUSIONS: The EMPARK system is a novel home monitoring system for providing patients with PD and the care team with feedback on longitudinal disease activities. User-centric development and evaluation of the system indicated high user acceptance and usability. The EMPARK infrastructure would empower patients and could be used for future applications in daily care and research.

The recent development in the data analytics field provides a boost in production for modern industries. Small-sized factories intend to take full advantage of the data collected by sensors used in their machinery. The ultimate goal is to minimize cost and maximize quality, resulting in an increase in profit. In collaboration with domain experts, we implemented a data visualization tool to enable decision-makers in a plastic factory to improve their production process. The tool is an interactive dashboard with multiple coordinated views supporting the exploration from both local and global perspectives. In summary, we investigate three different aspects: methods for preprocessing multivariate time series data, clustering approaches for the already refined data, and visualization techniques that aid domain experts in gaining insights into the different stages of the production process. Here we present our ongoing results grounded in a human-centered development process. We adopt a formative evaluation approach to continuously upgrade our dashboard design that eventually meets partners' requirements and follows the best practices within the field. We also conducted a case study with a domain expert to validate the potential application of the tool in the real-life context. Finally, we assessed the usability and usefulness of the tool with a two-layer summative evaluation that showed encouraging results.

The visualization of large multivariate networks (MVN) continues to be a great challenge and will probably remain so for a foreseeable future. The field of Multivariate Network Embedding seeks to meet this challenge by providing MVN-specific embedding technologies that targets different properties such as network topology or attribute values for nodes or links. Although many steps forward have been taken, the goal of efficiently embedding all aspects of a MVN remains distant. This position paper contrasts the current trend of finding new ways of jointly embedding several properties with the alternative strategy of instead using, and combining, already existing state-of-the-art single scope embedding technologies. From this comparison, we argue that the latter strategy provides a more generic and flexible approach with several advantages. Hence, we hope to convince the visual analytics community to invest more work in resolving some of the key issues that would make this methodology possible.

The ranking of authors is an important task within the field of sci- entometrics, and several different methods and criteria exist. In this poster abstract, we present an interactive visualization approach for exploring combinations of several different ranking criteria for a given set of publications and its associated co-author network. Ourvisualization tool allows the user to gain insights into the relative importance of individual authors as well as into the interdependency of different ranking criteria.

Natural language processing in combination with visualization can provide efficient ways to discover latent patterns of similarity which can be useful for exploring large sets of text documents. In this poster abstract, we describe the ongoing work on a visual analytics application, called SimBaTex, which is based on embedding technology, dynamic specification of similarity criteria, and a novel approach for similarity-based clustering. The goal of SimBaTex is to provide search-and-explore functionality to enable the user to identify items of interest in a large set of text documents by interactive assessment of both high-level similarity patterns and pairwise similarity of chosen texts.