Question and answer (Q&A) forums, as a way for seeking expertise on the Internet, have seen rapid growth in popularity in recent years. The expertise available on most such forums is voluntary, provided by individuals willing to invest their resources for no monetary remuneration. While these forums provide easy access to expertise, the expertise available is often lacking in quality and depth. Two major reasons for this are, the time investment required to participate in such forums, and the lack of a mechanism for identifying experts for specialized questions. We believe a Q&A recommender engine can ameliorate this problem significantly. The two primary contributions of this work are: a) a hierarchical Bayesian model based Q&A recommender, and b) a discussion of metrics to measure the performance of such a Q&A recommender. Two new metrics, responder load and questioner satisfaction, are suggested based on this discussion. These metrics are used to evaluate the performance of the recommender system on datasets harvested from the Yahoo! Answers website. / text