Utility-Based Multi-Stakeholder Recommendations by Multi-Objective Optimization

In the recommender systems, the receiver of the recommendations may not be the only stakeholder in the system, while others may come into play. For example, job positions cannot be simply recommended to a user according to his or her tastes only without considering the expectations of the recruiters. In this paper, we propose a utility-based recommendation model which produces recommendations by optimizing the utilities of multiple stakeholders. Particularly, we take advantage of the multi-criteria ratings that are associated with user expectations and evaluations. And we propose to learn the user expectations by the learning-to-rank approaches if they are unknown in the data. We also propose to seek the optimal solutions by using the multi-objective optimization techniques. Our experiments based on a speed-dating data set demonstrate the effectiveness of the proposed methods in which we are able to keep the balance between multiple utilities and the recommendation performance by adopting the multi-objective optimization.