Fig 3 - uploaded by Behnam Rahdari
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(a) Topic explanation containing the abstract of its Wikipedia entry and (b) Detailed view showing advisor's academic profile
Contexts in source publication
Context 1
... can add recommended topics to their profile with a click on the plus button. Selecting the question mark button next to the add button, opens up a separate topic explanation window containing the abstract of its Wikipedia entry (Figure 3a). ...
Context 2
... is also a relevance bar ( Figure 2E) to reflect the advisor's relevance to the user profile of interests and two action buttons. The button Details opens the Details view (Figure 3b) that provides more details about the advisor. The button Select adds the advisor to the final list of results ( Figure 2F). ...
Context 3
... button Select adds the advisor to the final list of results ( Figure 2F). Details view (Figure 3b) provides users with additional information about advisors such as affiliation, research impact, research interests, list of recent publications and external links to their research page and Google Scholar profile. To support the "interest discovery" process, these blue topic keywords can be added to the user's profile of interests and the short description of the topic can be reviewed with one click. ...
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Citations
... The academic recommendation has attracted a particular attention from the computer science community, especially with the proliferation of research-sharing platforms, such as arXiv, Semantic Scholar, and CiteSeerX; and academic social networks, such as ResearchGate and Academia.edu. Many research studies have been dedicated to explore various recommendation tasks related to the academic field, namely paper recommendation (Lee et al. 2013;Son and Kim 2018;Jiang et al. 2012;Sun et al. 2018;Bai et al. 2019;Kreutz and Schenkel 2022), book recommendation (Tewari et al. 2014;Milton et al. 2019;Bogaards and Schut 2021), citation recommendation (Jia and Saule 2017), event recommendation (Tsai and Brusilovsky 2017; Beierle et al. 2016), venue recommendation (Yu et al. 2018;Pradhan and Pal 2020), reviewer assignment problem (Dumais and Nielsen 1992), and academic collaborator/advisor recommendation (Lopes et al. 2010;Al-Ballaa et al. 2019;Rahdari et al. 2021). ...
Due to the exponentially increasing number of scholarly papers, academic information overload has caused significant difficulties for researchers. Papers recommendation is one of the methods proposed to help readers find interesting papers related to their work. According to a recent study by Nature journal, ResearchGate, with more than 15 million active members, is considered today as the largest academic social network. This platform offers scientists a multitude of functions, including sharing publications and projects, asking and answering questions, discussing research, establishing professional links, and making research visible. In this work, we propose a hybrid scientific paper recommendation system to help different actors on the ResearchGate professional network to carry out the various research and recruitment tasks. The proposed system combines three recommendation approaches, demographic-based, relationship network-based, and content-based techniques; and uses a dataset built by the authors. A data extraction API has been developed to build the experimental dataset containing most of the data types shared publicly on ResearchGate. Our dataset consists of more than 130000 researcher profiles, 15000 publications, and 1700 projects. The experiments carried out in this context, as well as the initial results, showed good performance, close to those obtained by the ResearchGate recommender system.
... Scrutability in RS refers to allowing users to correct their models when they disagree with (parts of) it or modify their models in order to adjust the recommendation results according to their needs and preferences [2,26]. The interest in providing scrutable user models has increased in the last decade and various studies have been conducted in this direction, presenting systems that enable scrutability and provide user control on the input layer of the RS [3,9,[32][33][34][35][36][37][38][39]. Explaining user models goes beyond just exposing and manipulating the user model to provide concrete explanations of how the user model was inferred. ...
This contribution sheds light on the potential of transparent user models for self-actualization. It discusses the development of EDUSS, a conceptual framework for self-actualization goals of transparent user modeling. Drawing from a qualitative research approach, the framework investigates self-actualization from psychology and computer science disciplines and derives a set of self-actualization goals and mechanisms. Following a human-centered design (HCD) approach, the framework was applied in an iterative process to systematically design a set of interactive visualizations to help users achieve different self-actualization goals in the scientific research domain. For this purpose, an explainable user interest model within a recommender system is utilized to provide various information on how the interest models are generated from users’ publication data. The main contributions are threefold: First, a synthesis of research on self-actualization from different domains. Second, EDUSS, a theoretically-sound self-actualization framework for transparent user modeling consisting of five main goals, namely, Explore, Develop, Understand, Scrutinize, and Socialize. Third, an instantiation of the proposed framework to effectively design interactive visualizations that can support the different self-actualization goals, following an HCD approach.
... Current scholarly search engines and bibliometric applications provide a wide variety of functionalities to support the exploration of research data and produce various kinds of analytics. These include Semantic Scholar, 1 Dimensions 2 Scopus, 3 Web of Science, 4 AMiner, 5 and many others. However, these tools only provide a limited set of analytics and metrics for assessing research conferences, limiting our ability to perform a comprehensive analysis of these events. ...
... Second, it characterises conferences according to 14K 6 CSO -https://cso.kmi.open.ac.uk/ research topics from the Computer Science Ontology (CSO). The reader notes that the CSO allows us to structure the research topics within the conferences according to a very granular representation [4]. For instance, the topic ''Machine Learning'' is composed of 760 more specific sub-topics, such as ''Denoising Autoencoders'' and ''Fuzzy Neural Networks''. ...
... The second part was a standard System Usability Scale (SUS) 44 [6] questionnaire to assess the usability of the AIDA dashboard. The third section asked the researchers to rate the quality of the analytics for the two chosen conferences on a [1][2][3][4][5] scale. The fourth part included seven open questions about strengths and weaknesses of the AIDA Dashboard. ...
Scientific conferences are essential for developing active research communities, promoting the cross-pollination of ideas and technologies, bridging between academia and industry, and disseminating new findings. Analyzing and monitoring scientific conferences is thus crucial for all users who need to take informed decisions in this space. However, scholarly search engines and bibliometric applications only provide a limited set of analytics for assessing research conferences, preventing us from performing a comprehensive analysis of these events. In this paper, we introduce the AIDA Dashboard, a novel web application, developed in collaboration with Springer Nature, for analyzing and comparing scientific conferences. This tool introduces three major new features: 1) it enables users to easily compare conferences within specific fields (e.g., Digital Libraries) and time-frames (e.g., the last five years); 2) it characterises conferences according to a 14K research topics from the Computer Science Ontology (CSO); and 3) it provides several functionalities for assessing the involvement of commercial organizations, including the ability to characterize industrial contributions according to 66 industrial sectors (e.g., automotive, financial, energy, electronics) from the Industrial Sectors Ontology (INDUSO). We evaluated the AIDA Dashboard by performing both a quantitative evaluation and a user study, obtaining excellent results in terms of quality of the analytics and usability.
Nowadays, it is very challenging and confusing for the users to identify a well-suited hotel according to their requirements. Hotels are also increasing tremendously with a broader range of amenities. Many applications exist that recommend hotels based on various criteria such as location, rating, best offer and prices, amenities. In this paper, to minimize time and cost, we propose an optimized recommendation system using machine learning model based on multiple criterion and various filtering methods. We simulate the performance of different machine learning (ML) models and select the best model for hotel recommendation based on multiple performance metrics.KeywordsRecommender systemHotel recommendersMachine learningSilhouette scoreDunn indexDavies BouldinMiniBatchK-meansBirchHierarchicalClustering
