Over the last few years, Web has changed significantly. Emergence of social networks and Web 2.0 have enabled people to interact with Web document in new ways not possible before. In this paper, we present PERSOSE a new search engine that personalizes the search results based on users' social actions. Although the users' social actions may sometimes seem irrelevant to the search, we show that they are actually useful for personalization. We propose a new relevance model called persocial relevance model utilizing three levels of social signals to improve the Web search. We show how each level of persocial model (users' social actions, friends' social actions and social expansion) can be built on top of the previous level and how each level improves the search results. Furthermore, we develop several approaches to integrate persocial relevance model into the textual Web search process. We show how PERSOSE can run effectively on 14 million Wikipedia articles and social data from real Facebook users and generate accurate search results. Using PERSOSE, we performed a set of experiments and showed the superiority of our proposed approaches. We also showed how each level of our model improves the accuracy of search results.