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Can Large Language Models Assess Serendipity in Recommender Systems?
In this investigation, a binary classification task was given to the LLMs to predict whether a user would find the recommended item serendipitously.
Hierarchical Matrix Factorization for Interpretable Collaborative Filtering
Central to our approach, called hierarchical embeddings, is the additional decomposition of the latent matrices (embeddings) into probabilistic connection matrices, which link the hierarchy, and a root cluster latent matrix.
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