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We describe an efficient framework for Web personalizationbased on sequential and non-sequential pattern discov-eryfrom usage data. Our experimental results performedon real usage data indicate that more restrictive patterns,such as contiguous sequential patterns (e.g., frequent navigationalpaths) are more suitable for predictive tasks, suchas Web prefetching, which involve predicting which item isaccessed next by a user), while less constrained patterns,such as frequent itemsets or general sequential patterns aremore effective alternatives in the context of Web personalizationand recommender systems.