Periodic web personalization aims to recommend the most relevant resources to a user during a specific time period by analyzing the periodic access patterns of the user from web usage logs. In this paper, we propose a novel web usage mining approach for supporting effective periodic web personalization. The proposed approach first constructs a user behavior model, called Personal Web Usage Lattice, from web usage logs using the fuzzy Formal Concept Analysis technique. Based on the Personal Web Usage Lattice, resources that the user is most probably interested in during a given period can be deduced efficiently. This approach enables the costly personalized resources preparation process to be done in advance rather than in real-time. The performance evaluation of the proposed periodic web personalization approach is also given in the paper.