Web usage models and profiles capture significant interests and trends from past accesses. They are used to improve user experience, say through recommendation of pages, pre-fetching of pages, etc. While browsing behavior changes dynamically over time, many web usage modeling techniques are static due to prohibitive model compilation times and also lack of fast incremental update mechanism. However, profiles have to be maintained so that they dynamically adapt to new interests and trends, since otherwise their use can lead to poor, irrelevant, and mis-targeted recommendations in personalization systems. We present a new profile maintenance scheme, which extends the Relational Fuzzy Subtractive Clustering (RFSC) technique and enables efficient incremental update of usage profiles. An impact factor is defined whose value can be used to decide the need for recompilation. The results from extensive experiments on a large real dataset of web logs show that the proposed maintenance technique, with considerably reduced computational costs, is almost as good as complete remodeling.