Knowledge Recommendation Services Based on Knowledge Interest Groups

Li, Hong; Liu, Lu; Lv, Chenggong

doi:10.1109/icsssm.2006.320606

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…As mentioned in the previous section, the users of the knowledge management system will encounter the problem of knowledge overload when a tremendous collection of knowledge is stored. The knowledge retrieving service could derive benefit from applying collaborative filtering or the content-based filtering to provide an automatic knowledge dissemination mechanism or a knowledge recommender service (Aryal, Dutta, and Morshed 2013;Choochaiwattana 2015;Huang et al 2012;Li, Liu, and LV 2006;Liang, Cai, and Zhao 2007;Si and Jin 2003;Vizcaino et al 2009;Zhao, Wang, and Lui 2009). There are only a few published papers that have focused on a combination of collaborative filtering and content-based filtering to develop the knowledge recommender service.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Knowledge Discovery System Based on Items and Tags

Niranatlamphong¹,

Choochaiwattana²

2009

J. Rev. Global Econ.

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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 tagbased 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.

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Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Knowledge Discovery System Based on Items and Tags

Niranatlamphong¹,

Choochaiwattana²

2009

J. Rev. Global Econ.

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“…It uses the user profile ontology as the basis to identify the interests of users. Li et al (2006) construct a knowledge recommendation services model based on knowledge interest groups in virtual community. Zhao et al (2009) designs a knowledge recommendation algorithm based on content syndication in knowledge sharing network.…”

Section: Introductionmentioning

confidence: 99%

An approach to task-oriented knowledge recommendation based on multi-granularity fuzzy linguistic method

Yuan

2015

Kybernetes

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Purpose -In organizations, knowledge intensive activities are mainly task oriented. Finding relevant completed tasks to the new task and providing task-related knowledge to workers facilitate the knowledge reuse. However, relevant tasks are not easily found in the huge amount of completed tasks. The purpose of this paper is to assist the worker to find the required knowledge for the task at hand by reusing the knowledge related to relevant competed tasks. Design/methodology/approach -First, the task profile is constructed. Relevant degrees to categories which tasks to are derived by multi-granularity fuzzy linguistic method. The stages of completed tasks are identified by the modified KNN method. Second, similar completed tasks on categories are retrieved and then the relevant tasks are selected from the retrieved similar tasks by multi-granularity fuzzy linguistic method. Third, the worker's current task stage is derived by calculating the similarity between the rated knowledge and the knowledge in the stage of completed tasks. Finally, the knowledge is recommend based on stage relevance, relevance of the completed tasks and importance of the knowledge. Findings -The proposed method helps the worker find the knowledge related to the task at hand by finding and reusing the completed tasks. The experimental results show that the proposed method performs well and can fulfill the worker's' knowledge needs. The use of the linguistic term set with preferred granularities instead of precise numbers facilitates the expression of the opinions. The recommendation stage by stage makes the knowledge recommended more precisely. The obtained linguistic weight of the knowledge makes the recommended results understood more easily than the numerical values. Research limitations/implications -In the study, the authors just focus on the codified knowledge recommendation. However, there is another kind of knowledge named tacit knowledge, which exists in the mind of the experts. The constructing and updating of the expert profile can be investigated. Meanwhile, the new recommendation method which considers more factors also needs to be studied further. Practical implications -The paper includes implications for the development of the knowledge management system. The proposed approach can be applied as a tool of knowledge sharing. It facilitates the finding of the knowledge that is related to the task at hand. Originality/value -The paper provides new ways to find the relevant tasks and the related knowledge to the task at hand. Meanwhile, the new method to recommend the knowledge stage by stage is also proposed. It expands the research in the knowledge sharing and knowledge recommendation.

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