This work addresses the difficult and underexplored case of supporting users who have very sparse interactions but post informative review texts.
This paper is available on arxiv under CC 4.0 license. Authors: Ghazaleh H. Torbati, Max Planck Institute for Informatics Saarbrucken, Germany & ghazaleh@mpi-inf.mpg.de; Andrew Yates, University of Amsterdam Amsterdam, Netherlands & a.c.yates@uva.nl; Anna Tigunova, Max Planck Institute for Informatics Saarbrucken, Germany & tigunova@mpi-inf.mpg.de; Gerhard Weikum, Max Planck Institute for Informatics Saarbrucken, Germany & weikum@mpi-inf.mpg.de.
with binary 0-or-1 gain and P@1 . We compute these by micro-averaging over all test items of all users. We also experimented with macro-averaging over users; as the results were not significantly different, we report only microaverage numbers in the paper. for each group separately. Note that this refinement drills down on the test outputs; the training is completely unaffected. • Items are split into unseen and seen items. The former consist of all test-time items that have not been seen at training time.
with binary 0-or-1 gain and P@1 . We compute these by micro-averaging over all test items of all users. We also experimented with macro-averaging over users; as the results were not significantly different, we report only microaverage numbers in the paper.
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