![](bg4.png)
results of previous evaluations of the KOs. This
formula, not explained due to space problems,
basically uses a pondered mean of the trust values
that other agents have about the KS.
4 CONCLUSIONS
CoPs are a means of knowledge sharing. However,
the knowledge that is reused should be valuable for
its members, who might otherwise prefer to ignore
the documents that a community has at its disposal.
In order to encourage the reuse of documents in
CoPs, in this work we propose a multi-agent
recommender system with which to suggest
trustworthy documents. Some of the advantages of
our system are:
The use of agents to represent members of the
community helps members to avoid the problem
of information overload since the system gives
agents the ability to reason about the
trustworthiness of the other agents or about the
recommendation of the most suitable documents
to the members of the community. Users are
not, therefore, flooded with all the documents
that exist with regard to a particular subject, but
their agents filter them and recommend only
those which are most trustworthy (when they
have rates) or those which are provided by more
trustworthy sources or sources which have
preferences and features that are similar to those
of the user in question.
The system can detect those users with the
greatest level of participation and those whose
documents have obtained higher rates. This
information can be used for two purposes:
expert detection and/or recognition of
fraudulent members who contribute with
worthless documents. Both functionalities imply
various advantages for any kind of organization,
i.e., the former permits the identification of
employee expertise and measures the quality of
their contributions, and the latter permits the
detection of fraud when users contribute with
non-valuable information.
The system facilitates the exchange and reuse of
information, since the most suitable documents
are recommended. The tool can also be
understood as a knowledge flow enabler
(Rodríguez-Elias et al, 2007), which encourages
knowledge reuse in companies.
Furthermore, the proposed algorithm is quite
flexible since in many situations weights are used to
modify the formulas. This algorithm could,
therefore, be used by the designers of other
recommender systems who could decide what values
they should give to these weights in order to adapt
the formula to their needs.
REFERENCES
Hansen, B. and Kautz, K., (2004), "Knowledge Mapping:
A Technique for Identifying Knowledge Flows in
Software Organisations", in Software Process
Improvement, LNCS 3281, Springer: 126-137.
Lawton, G., (2001), "Knowledge Management: Ready for
Prime Time?", in Computer, vol. 34(2): 12-14.
Rodríguez-Elias, O., Martínez-García, A., Vizcaíno, A.,
Favela, J., and Piattini, M. (2007). "A Framework to
Analyze Information Systems as Knowledge Flow
Facilitators", in Information Software Technology,
vol. 50(6): 481-498.
Soto, J. P., Vizcaíno, A., Portillo, J., and Piattini, M.,
(2007), "Applying Trust, Reputation and Intuition
Aspects to Support Virtual Communities of Practice",
in 11th International Conference on Knowledge-Based
and Intelligence Information and Engineering Systems
(KES), LNCS 4693, Springer: 353-360.
Ushida, H., Hirayama, Y., and Nakajima, H., (1998),
"Emotion Model for Life like Agent and its
Evaluation", in Proceedings of the Fifteenth National
Conference on Artificial Intelligence and Tenth
Innovative Applications of Artificial Intelligence
Conference (AAAI'98 / IAAI'98), Madison,
Wisconsin, USA.
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