Recommending products to new buyers is an important problem for online shopping services, since there are always new buyers joining a deployed system. In some recommender systems, a new buyer will be asked to indicate her/his preferences on some attributes of the product (like camera) in order to address the so called cold-start problem. Such collected preferences are usually not complete due to the user's cognitive limitation and/or unfamiliarity with the product domain, which are called partial preferences. The fundamental challenge of recommendation is thus that it may be difficult to accurately and reliably find some like-minded users via collaborative filtering techniques or match inherently preferred products with content-based methods. In this paper, we propose to leverage some auxiliary data of online reviewers' aspect-level opinions, so as to predict the buyer's missing preferences. The resulted user preferences are likely to be more accurate and complete. Experiment on a real user-study data and a crawled Amazon review data shows that our solution achieves better recommendation performance than several baseline methods.