Work

Recommendation of complementary material during chat discussions

Year: 2010

Type: article

Abstract: In the context of Internet, there are many tools that allow sharing knowledge. Examples of these tools are Web chats. However it is possible to use Web chats in a more effective way. In this sense, this paper presents a system that analyzes the themes discussed in a chat room and then recommends information sources according to the context of the discussion. In order to produce recommendations, the system considers users’ profiles to complement the knowledge of each individual, reaching what Vygotsky called zone of proximal development. Another important feature is related to the fact that, after the chat discussion session, it is possible to generate statistical analyses. These analyses allow evaluating the discussion (e.g. how many different subjects were discussed, discussion deviate) and thus the knowledge of the whole community and of each member (e.g. about what subject a participant is talking). The system uses text mining techniques to identify the themes discussed in the chat room.

Source: Knowledge Management & E-Learning An International Journal

DOI: 10.34105/j.kmel.2010.02.028

Authors: Stanley Loh, Daniel Lichtnow, Adriana Justin Cerveira Kampff, Jose Palazzo Moreira de Oliveira

Institutions: Universidade Católica de Pelotas, Universidade Federal do Rio Grande do Sul

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul

Award: G3747861814