Knowledge discovery and recommendation with linear mixed model
We give a concise tutorial on knowledge discovery with linear mixed model in movie
recommendation. The versatility of mixed effects model is well explained. Commonly used …
recommendation. The versatility of mixed effects model is well explained. Commonly used …
EchoMamba4Rec: Harmonizing Bidirectional State Space Models with Spectral Filtering for Advanced Sequential Recommendation
Y Wang, X He, S Zhu - arXiv preprint arXiv:2406.02638, 2024 - arxiv.org
Sequential recommendation aims to estimate dynamic user preferences and sequential
dependencies among historical user behaviors. Attention-based models have proven …
dependencies among historical user behaviors. Attention-based models have proven …
Sparse inversion for derivative of log determinant
S Zhu, AJ Wathen - arXiv preprint arXiv:1911.00685, 2019 - arxiv.org
Algorithms for Gaussian process, marginal likelihood methods or restricted maximum
likelihood methods often require derivatives of log determinant terms. These log …
likelihood methods often require derivatives of log determinant terms. These log …
A hybrid recommender system combing singular value decomposition and linear mixed model
T Zuo, S Zhu, J Lu - Intelligent Computing: Proceedings of the 2020 …, 2020 - Springer
We explain the basic idea of linear mixed model (LMM), including parameter estimation and
model selection criteria. Moreover, the algorithm of singular value decomposition (SVD) is …
model selection criteria. Moreover, the algorithm of singular value decomposition (SVD) is …
Accelerating DIN Model for Online CTR Prediction with Data Compression
Y Feng, S Zhu, Y Ou - 2022 7th International Conference on Big …, 2022 - ieeexplore.ieee.org
As the key task of recommender systems, the click-through rate (CTR) prediction is to predict
the probability of users clicking on a specific product. It is often costly due to the big data …
the probability of users clicking on a specific product. It is often costly due to the big data …
Personalized recommender systems with multi-source data
Pervasive applications of personalized recommendation models aim to seek a targeted
advertising strategy for business development and to provide customers with personalized …
advertising strategy for business development and to provide customers with personalized …
AIMS: Average information matrix splitting
S Zhu, T Gu, X Liu - arXiv preprint arXiv:1605.07646, 2016 - arxiv.org
For linear mixed models with co-variance matrices which are not linearly dependent on
variance component parameters, we prove that the average of the observed information and …
variance component parameters, we prove that the average of the observed information and …