To improve personalized search, we need to increase the efficiency of personalization models using effective user profiles and ranking models. The ranking models improve accuracy by combining personalized and non-personalized models. In the personalized models, user profiles are used to re-rank the results, while in non-personalized models documents are ranked in the absence of user profile. A personalization metric able to estimate the potential for personalization can enable the selective application of personalization and improve the overall effectiveness of the search system. In this paper, a personalization fuzzy topic model (FTM) is proposed for integrating the topical user profile into the personalized web search. The topical user profile is built using the fuzzy logic in handling the uncertainty of the occurrence of all topics in a document, and the fuzzy c-means algorithm is used to retrieve the relevant topics. To evaluate the proposed model, the ranking results using the proposed Personalized-FTM are compared against personalization using the Latent Dirichlet Allocation model. The result reveals that the Personalized-FTM improves the Mean Reciprocal Rank and the Normalized Discounted Cumulative Gain by 7% and 5%, respectively, for all topic numbers.