In recent years, predicting user behavior has drawn much attention in the fields of information retrieval. To that extend, many models and even more evaluation metrics have been proposed, aiming at the accurate evaluation of the information retrieval process. Most of the proposed metrics, including the well-known nDCG and ERR, rely on the assumption that the probability (R) a user finds a document relevant, depends only on its relevance grade. In this paper, we employ the assumption that this probability is a function of a combination of two factors; its relevance grade and its popularity grade. Popularity, as we define it from daily page views, can be considered as users’ vote for a document, and by combining this factor in the probability R we can capture user behavior more accurately. We present a new evaluation metric called Reciprocal Rank using Webpage Popularity (RRP) which takes into account not only the document’s relevance judgment, but also its popularity, and as a result correlates better with click metrics than the other evaluation metrics do.