IVOA Recommendation: IVOA Support Interfaces

This document describes the minimum interface that a (SOAP- or REST-based) web service requires to participate in the IVOA. Note that this is not required of standard VO services developed prior to this specification, although uptake is strongly encouraged on any subsequent revision. All new standard VO services, however, must feature a VOSI-compliant interface. This document has been produced by the Grid and Web Services Working Group. It has been reviewed by IVOA Members and other interested parties, and has been endorsed by the IVOA Executive Committee as an IVOA Recommendation. It is a stable document and may be used as reference material or cited as a normative reference from another document. IVOA's role in making the Recommendation is to draw attention to the specification and to promote its widespread deployment. This enhances the functionality and interoperability inside the Astronomical Community

NOTION OF EXPLAINABLE ARTIFICIAL INTELLIGENCE - AN EMPIRICAL INVESTIGATION FROM A USER\u27S PERSPECTIVE

The growing attention on artificial intelligence-based decision-making has led to research interest in the explainability and interpretability of machine learning models, algorithmic transparency, and comprehensibility. This renewed attention on XAI advocates the need to investigate end user-centric explainable AI, due to the universal adoption of AI-based systems at the root level. Therefore, this paper investigates user-centric explainable AI from a recommendation systems context. We conducted focus group interviews to collect qualitative data on the recommendation system. We asked participants about the end users\u27 comprehension of a recommended item, its probable explanation and their opinion of making a recommendation explainable. Our finding reveals end users want a non-technical and tailor-made explanation with on-demand supplementary information. Moreover, we also observed users would like to have an explanation about personal data usage, detailed user feedback, authentic and reliable explanations. Finally, we proposed a synthesized framework that will include end users in the XAI development process

Multi-Granularity Attention Model for Group Recommendation

Group recommendation provides personalized recommendations to a group of users based on their shared interests, preferences, and characteristics. Current studies have explored different methods for integrating individual preferences and making collective decisions that benefit the group as a whole. However, most of them heavily rely on users with rich behavior and ignore latent preferences of users with relatively sparse behavior, leading to insufficient learning of individual interests. To address this challenge, we present the Multi-Granularity Attention Model (MGAM), a novel approach that utilizes multiple levels of granularity (i.e., subsets, groups, and supersets) to uncover group members' latent preferences and mitigate recommendation noise. Specially, we propose a Subset Preference Extraction module that enhances the representation of users' latent subset-level preferences by incorporating their previous interactions with items and utilizing a hierarchical mechanism. Additionally, our method introduces a Group Preference Extraction module and a Superset Preference Extraction module, which explore users' latent preferences on two levels: the group-level, which maintains users' original preferences, and the superset-level, which includes group-group exterior information. By incorporating the subset-level embedding, group-level embedding, and superset-level embedding, our proposed method effectively reduces group recommendation noise across multiple granularities and comprehensively learns individual interests. Extensive offline and online experiments have demonstrated the superiority of our method in terms of performance

The Role of a Modern Organization Supporting Business Based on the Recommendation on the Example of the BNI Poland Group

The aim of the study is to analyze the functioning of the BNI Poland Group as an organization operating on the principle of networking and to show the role of recommendation in contemporary competitive conditions. The method of analyzing the academic literature, secondary sources, internal documents of the studied group as well as primary data in the form of a questionnaire have been used in the article. The article shows that a membership in a business support group based on the recommendation principle in the era of increased competition helps to remain on the market and raise the number of clients. This work can be used as a source of knowledge for every entrepreneur who is open to business contacts kept according to a strictly defined structure and procedures. The issue of the business support group on the basis of recommendation is quite a recent matter in Poland. The BNI Poland Group was established in 2010, while in the USA it has been functioning since 1985. Due to these dates, you can direct more attention to this form of marketing. This paper is not a commercial but scientific analysis of an organization which could be an instrument of competiveness at market