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.
Comparison of CUP against Baselines • LLM-Rec also performs poorly. Solely relying on the LLM’s latent knowledge about books is not sufficient when coping with long-tail items that appear sparsely in the LLM’s training data. Popularity and position bias further aggravate this adverse effect. Thus, to address RQ1 , merely and fully resorting to LLMs alone is not a viable solution. We also ran extended CUP configurations where precomputed CF vectors were incorporated .
Comparison of CUP against Baselines A. Comparison of CUP against Baselines • LLM-Rec also performs poorly. Solely relying on the LLM’s latent knowledge about books is not sufficient when coping with long-tail items that appear sparsely in the LLM’s training data. Popularity and position bias further aggravate this adverse effect. Thus, to address RQ1 , merely and fully resorting to LLMs alone is not a viable solution.
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