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.
This work addressed text-centric recommender systems in data-poor situations, specifically for the demanding case of book reviewing communities where many users have only few items and exhibit diverse tastes, but post text-rich comments. We presented a Transformer-based framework called CUP, with novel techniques for constructing concise user profiles by selecting informative pieces of user-side text.
This work addressed text-centric recommender systems in data-poor situations, specifically for the demanding case of book reviewing communities where many users have only few items and exhibit diverse tastes, but post text-rich comments. We presented a Transformer-based framework called CUP, with novel techniques for constructing concise user profiles by selecting informative pieces of user-side text.
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