Efficient sequential access pattern mining for web recommendations

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 reconstruction 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.