华东师范大学软件学院 王科强 (第一作者), 王晓玲

Presentation on theme: "华东师范大学软件学院 王科强 (第一作者), 王晓玲"— Presentation transcript:

1 华东师范大学软件学院 王科强 (第一作者), 王晓玲
Bayesian Probabilistic Multi-Topic Matrix Factorization for Rating Prediction 华东师范大学软件学院 王科强 (第一作者), 王晓玲 中国人民大学信息学院 赵鑫 Wayne Xin Zhao

2 推荐系统 User-item matrix m1 m2 u1 1 ? u2 5 u3 3 u4 2 Rating prediction

3 Rating Prediction User-based KNN Item-based KNN Matrix factorization
Feature-based MF (Context-based MF) SVD++ SVDFeature LibFM

4 推荐系统 User-item matrix m1 m2 u1 yes ? u2 u3 no u4 Item recommendation

5 Item recommendation User-based KNN Item-based KNN Matrix factorization
Pairwise-ranking recommendation

6 Our Focus Rating Prediction Matrix Factorization

7 最新的研究进展之一 稀疏性是困扰矩阵分解的一个主要问题之一 大部分评分矩阵只有少数ratings（相对比例）

8 最新的研究进展之一 Local Matrix Factorization (LMF) 缺乏一个较为形式化的刻画； 可解释性较差；

9 吸取前人工作的精华 Local Matrix Factorization
Each submatrix can be considered as a local cluster of users and items; A user or an item has multiple latent representations in different submatrices

10 Our Approach Cluster  Topic
Group items by topics Use topic models Cluster-specific latent factor  Topic-specific latent factor Each user or item has K topic-specific latent factors

11 Modeling the Rated Items
For each rating record <u,m>, we treat a user u as a document and an item m as a word token, then build the topic models

12 Modeling the Ratings Now we model the ratings

13 The generative process

14 Learning Algorithm

15 Connections with previous studies

16 Experiments Datasets Metric Baselines

17 Results

18 Quality of “clusters”

19 Summary Topics as “local clusters” Topic-specific latent factors
A bayesian approach

20 The End 谢谢！