Combining user interests with visited web page contents to perform contextual advertising enhances the user experience and adds more ad clicks, increasing revenue. The key issue is to improve the prediction accuracy of click rates for advertisements. The crucial challenges of the advertisement recommendation algorithm are the scalability on large number of users and web page contents, and the low prediction accuracy resulting from data sparsity. When data is large and sparse, the accuracy and efficiency of the traditional recommendation algorithms is poor. This paper proposes a factor model called AdRec. Based on the Unified Probability Matrix Factorization (UPMF), the model addresses the data sparsity problem by combining features of users, advertisements and web page contents to predict the click rate with higher accuracy. In addition, the computational complexity of our algorithm is linear with respect to the number of observed data, and scalable to very large datasets.