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.
System Architecture The CUP framework is based on a two-tower architecture for representation learning . The two towers are jointly trained, coupled by the shared loss function and ground truth. Figure 1 shows a pictorial overview. User reviews and items descriptions are fed into BERT followed by a feed-forward network to learn latent representations. Downstream, the vectors are simply compared by a dot product for scores that indicate whether the user likes an item or not.
System Architecture A. System Architecture The CUP framework is based on a two-tower architecture for representation learning . The two towers are jointly trained, coupled by the shared loss function and ground truth. Figure 1 shows a pictorial overview. User reviews and items descriptions are fed into BERT followed by a feed-forward network to learn latent representations.
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