核心思想：
（1）embedding network + random walk
（2）利用random walk 融合 structure, textual content and tag information for item
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Recommendation with Multi-Source Heterogeneous Information | |||||||||||
| 文献作者 | Li Gao | ||||||||||
| 文献发表年限 | 2018 | ||||||||||
| 文献关键字 | |||||||||||
| point-wise recommendation; 多信息融合； random walk | |||||||||||
| 摘要描述 | |||||||||||
| Network embedding has been recently used in social network recommendations by embedding low- dimensional representations of network items for recommendation. However, existing item recommendation models in social networks suffer from two limitations. First, these models partially use item information and mostly ignore important contextual information in social networks such as textual content and social tag information. Second, network embedding and item recommendations are learned in two independent steps without any interaction. To this end, we in this paper consider item recommendations based on heterogeneous information sources. Specifically, we combine item structure, textual content and tag information for recommendation. To model the multi-source heterogeneous information, we use two coupled neural networks to capture the deep network representations of items, based on which a new recommendation model Collaborative multi-source Deep Network Embedding (CDNE for short) is proposed to learn different latent representations. Experimental results on two real-world data sets demonstrate that CDNE can use network representation learning to boost the recommendation performance. | |||||||||||
