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This paper investigates the key techniques for implementing the distributed knowledge recommender model.
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A novel model of distributed knowledge recommender system is proposed to facilitate knowledge sharing among collaborative team members.
This paper proposes a model of the inner-enterprise knowledge recommender system.
This is what Professor Herring calls a "recommender model" of getting information.
By adjusting the rules, the configuration of the knowledge recommender system could be adapted to different users.
Comparing with traditional knowledge query way, the knowledge recommender systems supply us a more proactive way that could deliver the proper knowledge to the proper people at the proper time.
Among the P2P network of collaborative team members, each peer is deployed with one distributed knowledge recommender, which can supply proper knowledge resources to peers who may need them.
To promote a campaign, a recommender model is designed that filters campaigns to be promoted using association rule mining and based on backer's interests.
The advantage of the KNN algorithm over some other approaches is that the recommender model is transparent and easily understood - you can browse the Nearest Neighbour model and see the similarity values.
Here, recommendation systems also hold considerable potential for personalized advice on the basis of deeper and more complex information disclosed by the customer through so-called "knowledge based recommender applications" [ 8].
His research interests include configuration systems, recommender systems, model-based diagnosis, software requirements engineering, different aspects of human decision making, and knowledge acquisition methods.
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