Company recommendation for new graduates via implicit feedback multiple matrix factorization with Bayesian optimization

摘要

We have developed a recommendation system of companies for new graduates. In this paper, we defined high/low-browsed companies and constructed the recruitment navigation system of the low-browsed companies suitable for each student from the browsing data. Different from traditional recommendations, we need to deal with the problems that the entry (application) period is limited. The methods applicable to our problem tend to involve tuning many hyper parameters. We solved this problem by using Bayesian optimization. We empirically evaluated the system by using the entry data, which indicates which company a student has applied for. When we recommended 100 companies, our method covered over 45% of the companies that students applied to, while the existing methods covered only about 25%. Moreover, we found that although we did not validate our model using the entry data in the early stage of the recruitment activities, we can substitute the validation by using the browsing data in the Bayesian optimization.