TY - GEN
T1 - KNNs of Semantic Encodings for Rating Prediction
AU - Laugier, Leo
AU - Vadapalli, Raghuram
AU - Bonald, Thomas
AU - Dixon, Lucas
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - 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.
AB - 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.
KW - Graphs and net-works
KW - Natural Language Processing
KW - Web text analysis
U2 - 10.1109/CIC58953.2023.00020
DO - 10.1109/CIC58953.2023.00020
M3 - Conference contribution
AN - SCOPUS:85186667226
T3 - Proceedings - 2023 IEEE 9th International Conference on Collaboration and Internet Computing, CIC 2023
SP - 82
EP - 91
BT - Proceedings - 2023 IEEE 9th International Conference on Collaboration and Internet Computing, CIC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE International Conference on Collaboration and Internet Computing, CIC 2023
Y2 - 1 November 2023 through 3 November 2023
ER -