Incremental Relational Fuzzy Subtractive Clustering for Dynamic Web Usage Profiling

A primary application of web usage profiling is in model-based collaborative filtering (CF) for building recommender systems used for web personalization. CF techniques used for recommendation require accumulation of vast amount of historical user-preference information, which is queried to provide a personalized experience. Model-based CF techniques are preferred over the somewhat more accurate memory-based CF techniques primarily due to their higher efficiency and scalability. In the situation where user interests change dynamically with time, memory-based CF allows easy addition of new usage data, but model-based CF often requires complete remodeling operation. Being computationally intensive, remodeling is done occasionally and the models used normally lag behind the current usage patterns leading to irrelevant and mistargete