On Text-Based Estimation of Document Relevance
Eerika Savia, Samuel Kaski, Ville Tuulos and Petri Myllymäki
In: IJCNN 2004, 25 - 29 July 2004, Budapest, Hungary.
This work is part of a proactive information retrieval project that
aims at estimating relevance from implicit user feedback. The noisy
feedback signal needs to be complemented with all available
information, and textual content is one of the natural sources. Here
we take the first steps by investigating whether this source is at
all useful in the challenging setting of estimating the relevance of
a new document based on only few samples with known relevance. It
turns out that even sophisticated unsupervised methods like
multinomial PCA (or Latent Dirichlet Allocation) cannot help much.
By contrast, feature extraction supervised by relevant auxiliary
data may help.