Mining Negative Sequential Patterns for E-commerce Recommendations
Sue-Chen Hsueh, Ming-Yen Lin, Chien-Liang Chen · 2008
Sequential patterns in customer transactional databases are commonly mined for E-Commerce recommendations. In many practical applications, the absence of certain item-sets and sequences could have important implications. Mining frequent sequences comprising not only the occurrence but also the absence of certain sequences will increase the accuracy of product recommendations. A sequential pattern containing at least one absent itemset is called a negative sequential pattern. In this paper, we formulate the problem of negative sequential pattern mining by introducing practical constraints and propose an algorithm called PNSP for the mining. The discovered patterns can then be more interesting and effective to use. The experimental results show that PNSP may discover negative sequential patterns for practical E-commerce applications.