Hebbian Algorithms for a Digital Library Recommendation System

generally meta-data, so that documents on any specific subject can be transparently retrieved. While quality control can in principle still rely on the traditional methods of peer-refereeing and evaluation by experts that work relatively well with paper documents, retrieval on the basis of metadata has some intrinsic shortcomings, which can only get worse as the number of documents in the library increases. This paper proposes a set of algorithms to extract metadata about the documents in a digital library from the way these documents are used. Inspired by the learning of connections in the brain, the system assumes that documents develop stronger associations as they are more frequently co-activated. Co-activation corresponds to consultation by the same user, and decreases exponentially with the time interval between consultations. The strength of activation is proportional to the user’s interest for the document, either evaluated explicitly, or inferred implicitly from user actions...

• Type: Conference
• Year: 2002
• Where: ICPPW
• Authors: Francis Heylighen, Johan Bollen
• Keywords: Document, ICPPW 2002, Metadata, Paper Documents