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KNNs of Semantic Encodings for Rating Prediction | | Léo Laugier
; Thomas Bonald
; Lucas Dixon
; Raghuram Vadapalli
; | | Date: |
1 Feb 2023 | | 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. | | Source: | arXiv, 2302.00412 | | Services: | Forum | Review | PDF | Favorites | |
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