SARI OpenRec -- Empowering Recommendation Systems with Business Events

2010 ◽  
Author(s):  
Philip Limbeck ◽  
Martin Suntinger ◽  
Josef Schiefer
Keyword(s):  
Recommendation Systems ◽  
Business Events

Theoretizing A New Sustainable Business Event: Extension of Mair and Jago Model

2015 ◽  
Vol 3 (2) ◽  
pp. 55
Author(s):  
Norol Hamiza Zamzuri ◽  
Khairil Wahidin Awang ◽  
Yuhanis Abdul Aziz ◽  
Zaiton Samdin
Keyword(s):  
Social Impact ◽  
Geographical Area ◽  
Infrastructure Development ◽  
Structured Interviews ◽  
Sustainable Business ◽  
Knowledge Resources ◽  
Business Events ◽  
And Performance ◽  
Event Based

The growth of the event sector is underpinned by the demand of organizing a business event.  Thus, it leads to an increase in economic and social impact. However, the problems from the growth of this sector potentially results from the use of several event materials, transportation and infrastructure development.  Organizing a green event is seen as one of the strategies to reduce the environmental impact.  Therefore, the aim of this paper is to explore the issues involved throughout the process of greening an event by applying Mair and Jago Model.  Semi-structured interviews were conducted with event managers from six Malaysia business event companies that encourage green practices during their event.  Findings suggest that impact, initiative, support and performance motivates event organizers in organizing a green event.  It has also been found that knowledge, resources and behaviour are the barriers faced by event organizers throughout the process of organizing a green event.  Based on the findings it appears that two important factors have emerged from the data collection and analysis that showed a deviation from the Mair and Jago Model, namely “impact” for the motivation element and “support” for the barrier element.  The main limitation of this study was the scope of the study; as it only focuses on business events.  However, as the main purpose of this study is to explore the issues of organizing a green event, it has been found that there are other issues need to be explored in other contexts and geographical area.  Apart from this, as this is a case study, it can only replicate according to the circumstances of this case study. However, this study can be generalized in terms of the theory that has emerged from it.  It is suggested that further research should explore more issues in other contexts and geographical areas. 

Academic Events: An Empirically Grounded Typology and Their Academic Impact

2020 ◽  
Vol 24 (4) ◽  
pp. 481-497 ◽  
Author(s):  
Thomas Trøst Hansen ◽  
David Budtz Pedersen ◽  
Carmel Foley
Keyword(s):  
Analytical Framework ◽  
Future Research ◽  
Academic Impact ◽  
Future Research Agenda ◽  
Tourism Studies ◽  
Academic Events ◽  
Business Events ◽  
The Individual ◽  
Analytical Frameworks ◽  
Academic Sector

The meetings industry, government bodies, and scholars within tourism studies have identified the need to understand the broader impact of business events. To succeed in this endeavor, we consider it necessary to develop analytical frameworks that are sensitive to the particularities of the analyzed event, sector, and stakeholder group. In this article we focus on the academic sector and offer two connected analyses. First is an empirically grounded typology of academic events. We identify four differentiating dimensions of academic events: size, academic focus, participants, and tradition, and based on these dimensions we develop a typology of academic events that includes: congress, specialty conference, symposium, and practitioners' meeting. Secondly, we outline the academic impact of attending these four types of events. For this purpose, the concept of credibility cycles is used as an analytical framework for examining academic impact. We suggest that academic events should be conceptualized and evaluated as open marketplaces that facilitate conversion of credibility. Data were obtained from interviews with 22 researchers at three Danish universities. The study concludes that there are significant differences between the events in terms of their academic impact. Moreover, the outcome for the individual scholar depends on the investment being made. Finally, the study calls for a future research agenda on beyond tourism benefits based on interdisciplinary collaborations.

A feature-based hybrid recommender system for risk prediction : Machine learning approach (Preprint)

2020 ◽  
Author(s):  
Uzair Bhatti
Keyword(s):  
Machine Learning ◽  
Risk Prediction ◽  
Predictive Accuracy ◽  
Correct Diagnosis ◽  
Recommendation Systems ◽  
Data Integrity ◽  
Machine Learning Algorithms ◽  
Patient Counseling ◽  
Hybrid Filtering ◽  
Novel Algorithm

BACKGROUND In the era of health informatics, exponential growth of information generated by health information systems and healthcare organizations demands expert and intelligent recommendation systems. It has become one of the most valuable tools as it reduces problems such as information overload while selecting and suggesting doctors, hospitals, medicine, diagnosis etc according to patients’ interests. OBJECTIVE Recommendation uses Hybrid Filtering as one of the most popular approaches, but the major limitations of this approach are selectivity and data integrity issues.Mostly existing recommendation systems & risk prediction algorithms focus on a single domain, on the other end cross-domain hybrid filtering is able to alleviate the degree of selectivity and data integrity problems to a better extent. METHODS We propose a novel algorithm for recommendation & predictive model using KNN algorithm with machine learning algorithms and artificial intelligence (AI). We find the factors that directly impact on diseases and propose an approach for predicting the correct diagnosis of different diseases. We have constructed a series of models with good reliability for predicting different surgery complications and identified several novel clinical associations. We proposed a novel algorithm pr-KNN to use KNN for prediction and recommendation of diseases RESULTS Beside that we compared the performance of our algorithm with other machine algorithms and found better performance of our algorithm, with predictive accuracy improving by +3.61%. CONCLUSIONS The potential to directly integrate these predictive tools into EHRs may enable personalized medicine and decision-making at the point of care for patient counseling and as a teaching tool. CLINICALTRIAL dataset for the trials of patient attached

scholarly journals Recommendation Systems for Education: Systematic Review

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1611
Author(s):  
María Cora Urdaneta-Ponte ◽  
Amaia Mendez-Zorrilla ◽  
Ibon Oleagordia-Ruiz
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Formal Education ◽  
Research Work ◽  
Hybrid Approach ◽  
Recommendation Systems ◽  
Educational Resources ◽  
Future Research ◽  
Collaborative Approach ◽  
Developmental Approach

Recommendation systems have emerged as a response to overload in terms of increased amounts of information online, which has become a problem for users regarding the time spent on their search and the amount of information retrieved by it. In the field of recommendation systems in education, the relevance of recommended educational resources will improve the student’s learning process, and hence the importance of being able to suitably and reliably ensure relevant, useful information. The purpose of this systematic review is to analyze the work undertaken on recommendation systems that support educational practices with a view to acquiring information related to the type of education and areas dealt with, the developmental approach used, and the elements recommended, as well as being able to detect any gaps in this area for future research work. A systematic review was carried out that included 98 articles from a total of 2937 found in main databases (IEEE, ACM, Scopus and WoS), about which it was able to be established that most are geared towards recommending educational resources for users of formal education, in which the main approaches used in recommendation systems are the collaborative approach, the content-based approach, and the hybrid approach, with a tendency to use machine learning in the last two years. Finally, possible future areas of research and development in this field are presented.

scholarly journals Novel SDDM Rating Prediction Models for Recommendation Systems

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 101197-101206
Author(s):  
Diao Zhou ◽  
Shengnan Hao ◽  
Haiyang Zhang ◽  
Chenxu Dai ◽  
Yongli An ◽  
...  
Keyword(s):  
Prediction Models ◽  
Recommendation Systems ◽  
Rating Prediction
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