Traditional recommender systems suggest items by learning from user preferences, but ignore other stakeholders in the whole system. Actually, not only the receiver of the recommendations, but also other stakeholders may come into play, such as the producers of items or those of the system owners. Reciprocal recommender system in dating or job recommendations is one of these examples. However, we may have to simulate the utilities for each type of the stakeholder due to the utility definitions. In this paper, we perform exploratory analysis on a speed-dating data, where the user expectations are clearly defined. We try to build a multi-dimensional utility framework by utilizing multi-criteria ratings. We further analyze the relationship between the utilities and recommendation performance, and achieve a tradeoff as the optimal solution. Even more, the proposed approach is able to outperform the exiting reciprocal recommendation algorithms in precision, recall and overall utilities. Finally, we derive a promising way to define and optimize utilities to be generalized in other applications or domains.