March 13, 2024, 4:43 a.m. | Andreas Damianou, Francesco Fabbri, Paul Gigioli, Marco De Nadai, Alice Wang, Enrico Palumbo, Mounia Lalmas

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.07478v1 Announce Type: cross
Abstract: In the realm of personalization, integrating diverse information sources such as consumption signals and content-based representations is becoming increasingly critical to build state-of-the-art solutions. In this regard, two of the biggest trends in research around this subject are Graph Neural Networks (GNNs) and Foundation Models (FMs). While GNNs emerged as a popular solution in industry for powering personalization at scale, FMs have only recently caught attention for their promising performance in personalization tasks like ranking …

abstract art arxiv build consumption cs.ir cs.lg diverse foundation gnns graph graph neural networks information networks neural networks personalization regard research solutions state trends type

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