JSAI2018

Presentation information

Oral presentation

General Session » [General Session] 6. Web Mining

[1E2] [General Session] 6. Web Mining

Tue. Jun 5, 2018 3:20 PM - 5:00 PM Room E (4F Queen)

座長:池田 和史(KDDI綜合研究所)

4:20 PM - 4:40 PM

[1E2-04] Targets Recommendation Considering After M\&A Performance by Content based Neural Collaborative Filtering

〇Masanao Ochi1, Yasuko Yamano1, Kimitaka Asatani1, Akira Kitauchi2, Tomoyuki Ota2 (1. The University of Tokyo, 2. UZABASE Inc.)

Keywords:Recommendation

In this paper, we tackled the recommendation of the M\&A candidate considering the change in business performance after M\&A.
By incorporating the multitask learning framework into the Neural Collaborative Filtering method which is one of recommendation method using Deep Learning, we aimed to propose recommendation method considering the post-conversion change.
Experimental results show the similar accuracy as the simple logistic regression method.
By using this method, it will be possible to not only recommend M\&A targets but also to show to acquirers what kind of benefits they can obtain by acquiring.