Targets Recommendation Considering After M\&A Performance by Content based Neural Collaborative Filtering

Abstract

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.