BLOMA: Explain collaborative filtering via boosted local rank-one matrix approximation
Matrix Approximation (MA) is a powerful technique in recommendation systems. There are
two main problems in the prevalent MA framework. First, the latent factor is out of …
two main problems in the prevalent MA framework. First, the latent factor is out of …
BLOMA: Explain Collaborative Filtering via Boosted Local Rank-One Matrix Approximation
C Gao, S Yuan, Z Zhang, H Yin, J Shao - International Conference on …, 2019 - dl.acm.org
Matrix Approximation (MA) is a powerful technique in recommendation systems. There are
two main problems in the prevalent MA framework. First, the latent factor is out of …
two main problems in the prevalent MA framework. First, the latent factor is out of …
[PDF][PDF] BLOMA: Explain Collaborative Filtering via Boosted Local Rank-One Matrix Approximation
C Gao, S Yuan, Z Zhang, H Yin - researchgate.net
Matrix Approximation (MA) is a powerful technique in recommendation systems. There are
two main problems in the prevalent MA framework. First, the latent factor is out of …
two main problems in the prevalent MA framework. First, the latent factor is out of …
[PDF][PDF] BLOMA: Explain Collaborative Filtering via Boosted Local Rank-One Matrix Approximation
C Gao, S Yuan, Z Zhang, H Yin, J Shao - chongminggao.me
We illustrate three kinds of matrix factorization models. NMF and SVD can approximate the
matrix accurately, but cannot spot the three topics directly in their item factors as they just …
matrix accurately, but cannot spot the three topics directly in their item factors as they just …
BLOMA: Explain Collaborative Filtering via Boosted Local Rank-One Matrix Approximation
C Gao¹, S Yuan¹, Z Zhang¹, H Yin… - Database Systems for …, 2019 - books.google.com
Matrix Approximation (MA) is a powerful technique in recommendation systems. There are
two main problems in the prevalent MA framework. First, the latent factor is out of …
two main problems in the prevalent MA framework. First, the latent factor is out of …
[引用][C] BLOMA: explain collaborative filtering via Boosted Local rank-One Matrix Approximation
C Gao, S Yuan, Z Zhang, H Yin… - Lecture Notes in …, 2019 - espace.library.uq.edu.au
Matrix Approximation (MA) is a powerful technique in recommendation systems. There are
two main problems in the prevalent MA framework. First, the latent factor is out of …
two main problems in the prevalent MA framework. First, the latent factor is out of …
BLOMA: Explain Collaborative Filtering via Boosted Local Rank-One Matrix Approximation
C Gao, S Yuan, Z Zhang, H Yin - Springer
Matrix Approximation (MA) is a powerful technique in recommendation systems. There are
two main problems in the prevalent MA framework. First, the latent factor is out of …
two main problems in the prevalent MA framework. First, the latent factor is out of …