Abstract
We present a soft computing recommendation system named Talk Mine,to advance adaptive web and digital library technology. Talk Mine leads different databases or websites to learn new and adapt existing keywords to the categories recognized by its communities of users. It uses distributed artificial intelligence algorithms and soft computing technology. Talk Mine is currently being implemented for the research library of the Los Alamos National Laboratory under the Active Recommendation Project (http://arp.lanl.gov ).
Talk Mine is based on the integration of distributed knowledge networks using Evidence Sets, an extension of fuzzy sets. The identification of the interests of users relies on a process of combining several fuzzy sets into evidence sets, which models an ambiguous “and/or” linguistic expression. The interest of users is further finetuned by a human-machine conversation algorithm used for uncertainty reduction. Documents are retrieved according to the inferred user interests. Finally, the retrieval behavior of all users of the system is employed to adapt the knowledge bases of queried information resources. This adaptation allows information resources to respond well to the evolving expectations of users.
In this article the distributed architecture of Talk Mine is presented together with a description of its implementation in the Active Recommendation Project. In particular, the characterization of information resources as interacting distributed memory banks is presented. Evidence sets and the operations to produce them from several fuzzy sets are detailed. The conversation and adaptation algorithms used by TalkMine to interact automatically with users is described.
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Rocha, L.M. (2001). TalkMine: A Soft Computing Approach to Adaptive Knowledge Recommendation. In: Loia, V., Sessa, S. (eds) Soft Computing Agents. Studies in Fuzziness and Soft Computing, vol 75. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1815-4_4
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DOI: https://doi.org/10.1007/978-3-7908-1815-4_4
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