图 书 馆
异构信息网络中基于元图的推荐融合Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks |
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| 课程网址: | https://videolectures.net/videos/kdd2017_zhao_recommendation_fusi... |
| 主讲教师: | Huan Zhao |
| 开课单位: | KDD 2017研讨会 |
| 开课时间: | 2017-10-09 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | 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 €rst introducing the concept of meta-graph to HINbased 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 di‚erent meta-graph based features, we propose to use FM with Group lasso (FMG) to automatically learn from the observed ratings to e‚ectively select useful meta-graph based features. Experimental results on two real-world datasets, Amazon and Yelp, show the e‚ectiveness of our approach compared to stateof-the-art FM and other HIN-based recommendation algorithms. |
| 关 键 词: | 异构信息网络; 元图推荐融合; 推荐器 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2024-12-25：liyq |
| 最后编审: | 2024-12-25：liyq |
| 阅读次数: | 20 |