Hebbian algorithms for a digital library recommendation system

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 or the duration of the consultation. Co-activation values are added, producing a matrix of associations. This matrix can be used to recommend the documents that are most strongly related to a given document, most relevant to the user´s implicit interest profile, or most interesting to users overall. Moreover, it allows the calculation of document similarity values, which in turn can be used to cluster similar documents. The data needed to feed such a recommendation system are readily extracted from the usage logs of document servers, and can be processed either in a centralized or a distributed manner.