Efficient sequential access pattern mining for web recommendations

Abstract

Sequential access pattern mining discovers interesting and frequent user access patterns from web logs. Most of the previous studies have adopted Apriori-like sequential pattern mining techniques, which faced the problem on requiring expensive multiple scans of databases. More recent algorithms that are based on the Web Access Pattern tree (or WAP-tree) can achieve an order of magnitude faster than traditional Apriori-like sequential pattern mining techniques. However, the use of conditional search strategies in WAP-tree based mining algorithms requires re-construction of large numbers of intermediate conditional WAP-trees during mining process, which is also very costly. In this paper, we propose an efficient sequential access pattern mining algorithm, known as CSB-mine (Conditional Sequence Base mining algorithm). The proposed CSB-mine algorithm is based directly on the conditional sequence bases of each frequent event which eliminates the need for constructing WAP-trees. This can improve the efficiency of the mining process significantly compared with WAP-tree based mining algorithms, especially when the support threshold becomes smaller and the size of database gets larger. In this paper, the proposed CSB-mine algorithm and its performance will be discussed. In addition, we will also discuss a sequential access-based web recommender system that has incorporated the CSB-mine algorithm for web recommendations.