用图的模式，将user,item,attributes构建成图，然后再将点和边embed成vector，最后进行aggregation operation表达出最终的user和item，再嵌入MF等framework.
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks | |||||||||||
| 文献作者 | Huan Zhao and Quanming Yao and Jianda Li and Yangqiu Song and Dik Lun Lee | ||||||||||
| 文献发表年限 | 2017 | ||||||||||
| 文献关键字 | |||||||||||
| Meta-Graph; path-representation | |||||||||||
| 摘要描述 | |||||||||||
| Heterogeneous Information Network (HIN) is a natural and general representation of data in modern large commercial recommender systems which involve heterogeneous types of data. HIN based recommenders face two problems: how to represent the high-level semantics of recommendations and how to fuse the heterogeneous information to make recommendations. In this paper, we solve the two problems by first introducing the concept of meta-graph to HIN-based recommendation, and then solving the information fusion problem with a “matrix factorization (MF) + factorization machine (FM)” approach. For the similarities generated by each meta-graph, we perform standard MF to generate latent features for both users and items. With different meta-graph based features, we propose a group lasso regularized FM to automatically learn from the observed ratings to effectively select useful meta-graph based features. Experimental results on two real-world datasets, Amazon and Yelp, show the effectiveness of our approach compared to state-of-the-art FM and other HIN-based recommendation algorithms. | |||||||||||
