Developing a Web Recommendation System Based on Closed Sequential Patterns

Abstract

The proposed system is mainly based on mining closed sequential web access patterns. Initially, the PrefixSpan algorithm is employed on the preprocessed web server log data for mining sequential web access patterns. Subsequently, with the aid of post-pruning strategy, the closed sequential web access patterns are discovered from the complete set of sequential web access patterns. Then, a pattern tree, a compact representation of closed sequential patterns, is constructed from the discovered closed sequential web access patterns. The Patricia trie based data structure is used in the construction of the pattern tree. For a given user’s web access sequence, the proposed system provides recommendations on the basis of the constructed pattern tree. The experimentation of the proposed system is performed using synthetic dataset and the performance of the proposed recommendation system is evaluated with precision, applicability and hit ratio