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Gain fluctuation in microwave photonic phase shifters based on stimulated Brillouin scattering.

, , , , , and . WOCC, page 1-4. IEEE, (2016)

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Mixture-preference bayesian matrix factorization for implicit feedback datasets., and . SAC, page 1427-1434. ACM, (2020)Less is More: Reweighting Important Spectral Graph Features for Recommendation., , and . SIGIR, page 1273-1282. ACM, (2022)TSWNN+: Check-in Prediction Based on Deep Learning and Factorization Machine., , , and . Innovate-Data, volume 1054 of Communications in Computer and Information Science, page 65-79. Springer, (2019)How Powerful is Graph Filtering for Recommendation., , , and . CoRR, (2024)Gain fluctuation in microwave photonic phase shifters based on stimulated Brillouin scattering., , , , , and . WOCC, page 1-4. IEEE, (2016)Vector Representation Based Model Considering Randomness of User Mobility for Predicting Potential Users., , , and . PRIMA, volume 11224 of Lecture Notes in Computer Science, page 70-85. Springer, (2018)Less is More: Removing Redundancy of Graph Convolutional Networks for Recommendation., , and . ACM Trans. Inf. Syst., 42 (3): 85:1-85:26 (May 2024)A Robust Hierarchical Graph Convolutional Network Model for Collaborative Filtering., and . CoRR, (2020)SVD-GCN: A Simplified Graph Convolution Paradigm for Recommendation., , and . CIKM, page 1625-1634. ACM, (2022)Balancing Embedding Spectrum for Recommendation., , , and . CoRR, (2024)