本文在应用上没有太多的创新，但在方法上有一点的技术含量。
主要思想：
将原来MF中静态的U和V变成与时间戳t相关的动态的embeddings。也就是随着t的变化，U和V是变化的。
主要是通过假设U^{t_1}是通过U^t的表达变化而来。即原来的高斯分布中乘以另一高斯。
不过本文中的解法可以进一步学习，参考。
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Dynamic Bayesian Logistic Matrix Factorization for Recommendation with Implicit Feedback | |||||||||||
| 文献作者 | Yong Liu | ||||||||||
| 文献发表年限 | 2018 | ||||||||||
| 文献关键字 | |||||||||||
| time; temporal; implicit feedback; Gaussian process | |||||||||||
| 摘要描述 | |||||||||||
| Matrix factorization has been widely adopted for recommendation by learning latent embeddings of users and items from observed user-item interaction data. However, previous methods usually assume the learned embeddings are static or homogeneously evolving with the same diffusion rate. This is not valid in most scenarios, where users’ preferences and item attributes heterogeneously drift over time. To remedy this issue, we have proposed a novel dynamic matrix factorization model, named Dynamic Bayesian Logistic Matrix Factorization (DBLMF), which aims to learn heterogeneous user and item embeddings that are drifting with inconsistent diffusion rates. More specifically, DBLMF extends logistic matrix factorization to model the probability a user would like to interact with an item at a given timestamp, and a diffusion process to connect latent embeddings over time. In addition, an efficient Bayesian inference algorithm has also been proposed to make DBLMF scalable on large datasets. The effectiveness of the proposed method has been demonstrated by extensive experiments on real datasets, compared with the state-of-the-art methods. | |||||||||||
