（1）本文所谓的Metadata等价于user/item content based information （离散的？有点像每个user/item都有一个属性集合）, such age, location，所以本文是content + CF的推荐
（2）本文采用的基本技术方案：Poisson Factorization（概率图模型，有点像PMF），只有生成的过程不一样。会给离散的feature通过Gamma生成weights，类似的，逐渐组合生成最后的评分。
（3）最后利用VI学出参数。
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Metadata-dependent Infinite Poisson Factorization for Efficiently Modelling Sparse and Large Matrices in Recommendation | |||||||||||
| 文献作者 | Trong Dinh Thac Do | ||||||||||
| 文献发表年限 | 2018 | ||||||||||
| 文献关键字 | |||||||||||
| Poisson Factorization; content-based + CF; Gamma; graphical models; VI (Variational Inference)；属性集合 | |||||||||||
| 摘要描述 | |||||||||||
| Matrix Factorization (MF) is widely used in Recommender Systems (RSs) for estimating missing ratings in the rating matrix. MF faces major challenges of handling very sparse and large data. Poisson Factorization (PF) as an MF variant addresses these challenges with high efficiency by only computing on those non-missing elements. However, ignoring the missing elements in computation makes PF weak or incapable for dealing with columns or rows with very few observations (corresponding to sparse items or users). In this work, Metadata-dependent Poisson Factorization (MPF) is invented to address the user/item sparsity by integrating user/item metadata into PF. MPF adds the metadata-based observed entries to the factorized PF matrices. In addition, similar to MF, choosing the suitable number of latent components for PF is very expensive on very large datasets. Accordingly, we further extend MPF to Metadata-dependent Infinite Poisson Factorization (MIPF) that integrates Bayesian Nonparametric (BNP) technique to automatically tune the number of latent components. Our empirical results show that, by integrating metadata, MPF/MIPF sig- nificantly outperform the state-of-the-art PF models for sparse and large datasets. MIPF also effectively estimates the number of latent components. | |||||||||||
