Implementation of a Training Courses Recommender System based on k-means algorithm

Providing the right professional training courses for employees is a critical issue for organizations as well as employees. Its necessity stemmed out on the fulfillment of the organization and employees need. Thus, building a recommender system that would help in the decision making process and planning of the training course offered by organizations. This can be performed using various techniques and methodologies, where the most important one is data mining. Data mining is a process of looking for specific patterns and knowledge from large databases and carrying out predictions for outputs. Therefore, this project aims to build a web-based application for predicting appropriate training recommenders for Princess Norah University employees based on their education and professional information. This helps the university in suggesting the most optimal training recommender for employees, which in turn can enhance their performance and develop their career and working levels. Employees’ data was gathered from the Human Resource of the university and then clustered using the WEKA program to find the centroids of clusters to be then used in the developed application. The developed web-based application is used to suggest the most suitable training recommender for each employee. Results demonstrate that the developed web-based application effectively suggests the most appropriate training courses for employees based on the previously taken courses, evaluation of courses and probability for promotion. Furthermore, this web-based application can be used for describing the appropriate training courses for new employees based on their levels. The achieved accuracy of the developed system was 73.33%

State of the art of a multi-agent based recommender system for active software engineering ontology

Software engineering ontology was first developed to provide efficient collaboration and coordination among distributed teams working on related software development projects across the sites. It helped to clarify the software engineering concepts and project information as well as enable knowledge sharing. However, a major challenge of the software engineering ontology users is that they need the competence to access and translate what they are looking for into the concepts and relations described in the ontology; otherwise, they may not be able to obtain required information. In this paper, we propose a conceptual framework of a multi-agent based recommender system to provide active support to access and utilize knowledge and project information in the software engineering ontology. Multi-agent system and semantic-based recommendation approach will be integrated to create collaborative working environment to access and manipulate data from the ontology and perform reasoning as well as generate expert recommendation facilities for dispersed software teams across the sites

Sistema de recomendação de serviços para uma cidade inteligente

Cidades Inteligentes são espaços complexos com variedade de informações, no qual os cidadãos podem ter acesso, sejam eles visitantes ou residentes. Denominado de pontos de interesse (POIs) as informações relacionadas a uma cidade, estando estes POIs espalhados ou concentrados no mesmo local. Ao buscar interesses, o usuário utiliza meios tradicionais de pesquisa, que por vezes não retornam exatamente o local ou serviço desejado. Visando melhorar o resultado da busca, foi utilizado um modelo ontológico, através de um estudo de caso, adicionando dados da cidade de Cotiporã/RS. Através deste estudo é possível mostrar que uma das formas de se modelar um sistema de recomendação é com base nas ontologias. Essas melhoram a semântica e criam uma padronização na web para facilitar o compartilhamento do conhecimento. A abordagem desenvolvida tem por objetivo recomendar POIs turísticos e serviços da área da saúde. Para avaliar o protótipo, foram desenvolvidas regras SWRL inferidas utilizando o motor de inferências Pellet, onde, baseado nas preferências do usuário, o protótipo retorna o resultado da busca efetuada através da localização do usuário, apresentando na interface. Com esta proposta é possível continuar o projeto ampliando os filtros de recomendação para outros setores e ampliar o projeto para outras cidades.Smart Cities are complex spaces with a variety of information, which citizens can access, whether they are visitors or residents. Information related to a city is referred to as points of interest (POIs), these POIs being scattered or concentrated in the same location. When seeking interests, the user uses traditional means of research, which sometimes do not return exactly the desired location or service. To improve the search result, an ontological model was used, through a case study, adding data from the city of Cotiporã / RS. Through this study it is possible to show that one of the ways to model a recommendation system is based on ontologies. These improve semantics and create a standardization on the web to facilitate knowledge sharing. The developed approach aims to recommend tourist POIs and health services. To evaluate the prototype, SWRL rules developed using the Pellet inferences engine were developed, where, based on the user's preferences, the prototype returns the search result made through the user's location, presenting it on the interface. With this proposal it is possible to continue the project by expanding the recommendation filters to other sectors and to expand the project to other cities

A framework for active software engineering ontology

The passive structure of ontologies results in the ineffectiveness to access and manage the knowledge captured in them. This research has developed a framework for active Software Engineering Ontology based on a multi-agent system. It assists software development teams to effectively access, manage and share software engineering knowledge as well as project information to enable effective and efficient communication and coordination among teams. The framework has been evaluated through the prototype system as proof-of-concept experiments