Recommender System Based on Expert and Item Category

The objective of this study was to introduce the recommender system based on expert and item category to match the right items to users. In this study, the expert identification was divided into 3 techniques which were 1) the experts from social network technique   2) the experts from the frequency of rating technique and 3) the experts from other user’s preferences. To filter the expert users by using the frequency of rating technique and the experts from other user’s preferences technique, data about item category is used. For evaluation in this study, the researcher used Epinion for the performance testing to find out errors and accuracies in the prediction process. The results of this study showed that all the presented techniques had mean absolute error score at 0.15 and 85 percentages of accuracy, especially the expert identification combining with item category, it can reduce 60 percentages of the duration of recommendation creatingThe objective of this study was to introduce the recommender system based on expert and item category to match the right items to users. In this study, the expert identification was divided into 3 techniques which were 1) the experts from social network technique, 2) the experts from the frequency of rating technique, and 3) the experts from other user’s preferences. To filter the expert users by using the frequency of rating technique and the experts from other user’s preferences technique, data about item category is used. For evaluation in this study, the researcher used Epinion for the performance testing to find out errors and accuracies in the prediction process. The results of this study showed that all the presented techniques had mean absolute error score at 0.15 and 85 percentages of accuracy, especially the expert identification combining with item category, it can reduce 60 percentages of the duration of recommendation creating

Um modelo para extração de perfil de especialista aplicado às ferramentas de expertise location e apoio à Gestão do Conhecimento

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia e Gestão do Conhecimento, Florianópolis, 2016.As ferramentas de Expertise Location podem ser utilizadas amplamente na Gestão do Conhecimento para apoiar a identificação e o compartilhamento do conhecimento. Porém, manter os dados dos colaboradores de uma organização atualizados nessas ferramentas pode ser desafiador. Muitas vezes, os colaboradores precisam preencher as mesmas informações em diversos sistemas. Como uma abordagem alternativa para simplificar esse processo de atualização dos dados, este trabalho propõe um modelo para a extração automática de perfis de especialistas a partir de seus documentos não estruturados. Assim, realizou-se uma pesquisa aplicada e exploratória com base em uma revisão integrativa da literatura, a qual resultou na identificação das abordagens atuais para a extração de perfil de especialista que permitisse a construção desse modelo. A partir dessas abordagens, foram elaborados um modelo conceitual e um protótipo baseados em Processamento de Linguagem Natural para a tarefa de extração de informações de perfil de especialistas que possam fornecer insumos para a identificação de seus conhecimentos e de suas áreas de interesse. A implementação do protótipo resultou também em uma ferramenta de código aberto. Tal ferramenta é disponibilizada em um site público, em conjunto com o seu código-fonte, e gera uma página de perfil com o uso de componentes de tag cloud e timeline. Com o intuito de verificar a viabilidade do modelo proposto, a partir de documentos de voluntários, foram executados testes comparando os perfis gerados pela ferramenta com os perfis presentes na rede social LinkedIn. Os resultados dos testes demonstraram que o modelo proposto pode representar uma alternativa viável para a geração de perfis de especialistas de forma automática com o objetivo de apoiar as ferramentas de Expertise Location em uma organização. Consequentemente, a adoção desse modelo pode reduzir a necessidade de atualizações constantes dos perfis de especialistas de forma manual.Abstract : The Expertise Location Tools can be widely used in Knowledge Management in order to support the identification and sharing of the knowledge. However, to keep the data of the employees of an organization updated in those tools can be challenging. From time to time, employees need to fill out the same data in different systems. As an alternative approach to simplify this process of updating the data, this paper proposes a model for the automatic extraction of expert profiles from their own non-structural documents. Thus, an applied and exploratory research based on an integrative literature review was carried out, resulting in the identification of the current approaches to the extraction of an expert profile that could allow the construction of this model. From these approaches were elaborated a conceptual model and a prototype based on Natural Language Processing for the task of extraction of information from expert profiles that could provide inputs to the identification of their expertise and their areas of interest. The prototype implementation has also resulted in an open source tool. This tool is available on a public website together with its source code and it generates a profile page using the tag cloud and timeline components. In order to verify the feasibility of the proposed model, tests from documents of volunteers were performed comparing the profiles generated by the tool with those profiles on LinkedIn social network. The test results demonstrated that the proposed model can represent a viable alternative to the generation of automatically expert profiles in order to support Expertise Location tools in an organization. Consequently, the adoption of this model can reduce the need for constant updates of the expert profiles

Expertise Recommender System for Scientific Community

Finding experts in academics as well as in enterprises is an important practical problem. Both manual and automated approaches are employed and have their own pros and cons. On one hand, the manual approaches need extensive human efforts but the quality of data is good, on the other hand, the automated approaches normally do not need human efforts but the quality of service is not as good as in the manual approaches. Furthermore, the automated approaches normally use only one metric to measure the expertise of an individual. For example, for finding experts in academia, the number of publications of an individual is used to discover and rank experts. This paper illustrates both manual and automated approaches for finding experts and subsequently proposes and implements an automated approach for measuring expertise profile in academia. The proposed approach incorporates multiple metrics for measuring an overall expertise level. To visualize a rank list of experts, an extended hyperbolic visualization technique is proposed and implemented. Furthermore, the discovered experts are pushed to users based on their local context. The research has been implemented for Journal of Universal Computer Science (J. UCS) and is available online for the users of J.UCS