Distributed recommender for peer-to-peer knowledge sharing

Abstract

A novel model of distributed knowledge recommender system is proposed to facilitate knowledge sharing among collaborative team members. Different from traditional recommender systems in the client–server architecture, our model is oriented to the peer-to-peer (P2P) environment without the centralized control. 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. This paper investigates the key techniques for implementing the distributed knowledge recommender model. Moreover, a series of simulation-based experiments are conducted by using the data from a real-world collaborative team in an enterprise. The experimental results validate the efficiency of the proposed model. This research paves the way for developing platforms that can share and manage large-scale distributed knowledge resources. This study also provides a new framework for simulating and studying individual or organizational behaviors of knowledge sharing in a collaborative team.