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arXiv · 2302.00412

KNNs of Semantic Encodings for Rating Prediction

Léo Laugier·Raghuram Vadapalli·Thomas Bonald·Lucas Dixon
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Abstract

This paper explores a novel application of textual semantic similarity to user-preference representation for rating prediction. The approach represents a user's preferences as a graph of textual snippets from review text, where the edges are defined by semantic similarity. This textual, memory-based approach to rating prediction enables review-based explanations for recommendations. The method is evaluated quantitatively, highlighting that leveraging text in this way outperforms both strong memory-based and model-based collaborative filtering baselines.

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Léo Laugier, Raghuram Vadapalli, Thomas Bonald, Lucas Dixon. 2023-02-01. KNNs of Semantic Encodings for Rating Prediction. https://arxiv.org/abs/2302.00412

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