In the present world internet and web search engines have become an important part in one's day-to-day life. For a user query, more than few thousand web pages are retrieved but most of them are irrelevant. A major problem in search engine is that the user queries are usually short and ambiguous, and they are not sufficient to satisfy the precise user needs. Also listing more number of results according to user make them worry about searching the desired results and it takes large amount of time to search from the huge list of results. To overcome all the problems, an effective approach is developed by capturing the users' click through and bookmarking data to provide personalized query recommendation. For retrieving the results, Google API is used. Experimental results show that the proposed method is providing better query recommendation results than the existing query suggestion methods.