Nowadays, the well-known search engines, such as Google, Yahoo, MSN, etc, have provided the users with good search results based on special search strategies. However there still exist some problems unsolved for traditional search engines, including: 1) the gap between user's intention and searched results is not easy to narrow down under the global search space, and 2) user's interested pages hidden in the local website are not associated with the search results. To deal with such problems, in this paper, we propose a novel approach for personalized page ranking and recommendation by integrating association mining and PageRank so as to meet user's search goals. Moreover, by mining the users' browsing behaviors, we can successfully bridge the gap between global search results and local preferences. The effectiveness of our proposed approach was verified through experimental evaluations.