Lifescience Global

Abstract - A Hybrid Knowledge Discovery System Based on Items and Tags

Journal of Reviews on Global Economics

A Hybrid Knowledge Discovery System Based on Items and Tags
Pages 321-327

Winyu Niranatlamphong and Worasit Choochaiwattana

DOI: https://doi.org/10.6000/1929-7092.2017.06.32

Published: 09 June 2017 


Abstract: Exponentially increasing knowledge in a management system is the main cause of the overload problem. Development of a recommender service embedded in the management system is challenging. This paper proposes a hybrid approach by combining an item-based recommendation technique (collaborative filtering technique) with a tag-based recommendation technique (content based filtering technique). In order to evaluate the performance of the proposed hybrid approach, a group of knowledge management system users are invited as participants in the research. Participants are asked to use the prototype of a management system embedded within the knowledge recommender service for four months, which guarantees that each interaction by participants with knowledge items are recorded. A confusion matrix is used to compute accuracy of the proposed hybrid approach. The results of the experiments reveal that the hybrid approach outperforms both item-based and tag-based approaches. The hybrid approach seems to be a promising technique for a recommender service in the knowledge management system.

Keywords: Collaborative Filtering, Content-based Filtering, Item-Based Recommendation, Tag-Based Recommendation, Knowledge Recommender Service.

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