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Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Information Science and Technology / Faculty of Information Science and Technology >
Peer-reviewed Journal Articles, etc >
Link Prediction Using Higher-Order Feature Combinations across Objects
| Title: | Link Prediction Using Higher-Order Feature Combinations across Objects |
| Authors: | Atarashi, Kyohei Browse this author | | Oyama, Satoshi Browse this author →KAKEN DB | | Kurihara, Masahito Browse this author →KAKEN DB |
| Keywords: | link prediction | | higher-order feature combinations | | bilinear model | | factorization machines | | matrix factorization |
| Issue Date: | Aug-2020 |
| Publisher: | 電子情報通信学会(The Institute of Electronics, Information and Communication Engineers / IEICE) |
| Journal Title: | IEICE transactions on information and systems |
| Volume: | E103D |
| Issue: | 8 |
| Start Page: | 1833 |
| End Page: | 1842 |
| Publisher DOI: | 10.1587/transinf.2019EDP7266 |
| Abstract: | Link prediction, the computational problem of determining whether there is a link between two objects, is important in machine learning and data mining. Feature-based link prediction, in which the feature vectors of the two objects are given, is of particular interest because it can also be used for various identification-related problems. Although the factorization machine and the higher-order factorization machine (HOFM) are widely used for feature-based link prediction, they use feature combinations not only across the two objects but also from the same object. Feature combinations from the same object are irrelevant to major link prediction problems such as predicting identity because using them increases computational cost and degrades accuracy. In this paper, we present novel models that use higher-order feature combinations only across the two objects. Since there were no algorithms for efficiently computing higher-order feature combinations only across two objects, we derive one by leveraging reported and newly obtained results of calculating the ANOVA kernel. We present an efficient coordinate descent algorithm for proposed models. We also improve the effectiveness of the existing one for the HOFM. Furthermore, we extend proposed models to a deep neural network. Experimental results demonstrated the effectiveness of our proposed models. |
| Rights: | Copyright ©2020 The Institute of Electronics, Information and Communication Engineers | | https://search.ieice.org/ |
| Type: | article |
| URI: | http://hdl.handle.net/2115/79756 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: 小山 聡
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