论文认为（假设核心）：不同领域当中，用户的相似性是一致的。即两个用户在不同的领域的相似度基本一致。
然后利用NMF求解。（见附件文档）
所谓的Semi-Supervised NMF: “We consider using the overlapping population across latforms as supervisory information”
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Little Is Much: Bridging Cross-Platform Behaviors through Overlapped Crowds | |||||||||||
| 文献作者 | Meng Jiang, Peng Cui | ||||||||||
| 文献发表年限 | 2016 | ||||||||||
| 文献关键字 | |||||||||||
| cross-domain; overlapped; transfer；semi- supervised NMF | |||||||||||
| 摘要描述 | |||||||||||
| People often use multiple platforms to fulfill their different information needs. With the ultimate goal of serving people intelligently, a fundamental way is to get comprehensive understanding about user needs. How to organically integrate and bridge cross-platform information in a human-centric way is important. Existing transfer learning assumes either fully- overlapped or non-overlapped among the users. However, the real case is the users of different platforms are partially over- lapped. The number of overlapped users is often small and the explicitly known overlapped users is even less due to the lacking of unified ID for a user across different platforms. In this paper, we propose a novel semi-supervised transfer learning method to address the problem of cross-platform behavior prediction, called XPT RANS . To alleviate the sparsity issue, it fully exploits the small number of overlapped crowds to optimally bridge a user’s behaviors in different platforms. Extensive experiments across two real social networks show that XPT RANS significantly outperforms the state-of-the-art. We demonstrate that by fully exploiting 26% overlapped users, XPT RANS can predict the behaviors of non-overlapped users with the same accuracy as overlapped users, which means the small overlapped crowds can successfully bridge the information across different platforms. | |||||||||||
