Impact of Site Characteristics on Recommendation Models Based On Association Rules and Sequential Patterns

Abstract

A number of studies have suggested the use of discovered Web usage patterns such as association rules, general sequential patterns, and contiguous sequential patterns (frequent navigational paths) for generating recommendations in personalization systems. To-date, however, no studies have considered the conditions under which recommendation models based on sequential patterns may be more appropriate for personalization as compared to those based on non-sequential patterns (such as frequent itemsets). We conjecture that the structural characteristics of Web sites, such as the site topology and the degree of connectivity, have a significant impact on the relative performance of these recommendation models. We present a framework forWeb personalization based on association rules, contiguous and non-contiguous sequential patterns discovered from Web usage data. We then conduct a detailed comparative evaluation based on real Web usage data from three sites with different structural characteristics. Our results suggest that less constrained patterns, such as frequent itemsets, are better suited for personalization in sites with a higher degree of connectivity and shorter navigational depth, while the sequential recommendation models may be more suitable in sites with deeper navigational depth or in sites relying on many dynamically generated pages.