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http://hdl.handle.net/10397/93357
| Title: | Recommendation as a service in mergers and acquisitions transactions | Authors: | Yang, YC Ke, YS Wu, W Lin, KP Jin, Y |
Issue Date: | 2019 | Source: | Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2019, v. 11589, p. 151-159 | Abstract: | Mergers and acquisitions (M&A) happens frequently between corporations to combine and/or transfer their ownerships, operating units and assets. The purpose of the study is to develop a service that is able to recommend a feasible M&A deal. We integrate the support vector machine model with the kernel tricks to automatically determine M&A deals. In the end of the study, our proposed technique is empirically validated, and the results show the effectiveness of the recommendation service. | Keywords: | Financial kernel Machine learning Mergers and acquisitions Recommendation service Support vector machine |
Publisher: | Springer | Journal: | Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) | ISSN: | 0302-9743 | EISSN: | 1611-3349 | DOI: | 10.1007/978-3-030-22338-0_12 | Description: | 6th International Conference on HCI in Business, Government, and Organizations, HCIBGO 2019, held as part of the 21st International Conference on Human-Computer Interaction, HCI International 2019, Orlando, FL, USA, July 26-31, 2019 | Rights: | © Springer Nature Switzerland AG 2019. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-030-22338-0_12 |
| Appears in Collections: | Conference Paper |
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| Jin_Recommendation_As_Service.pdf | Pre-Published version | 860.59 kB | Adobe PDF | View/Open |
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