Combining sequential patterns and association rules for use in e-shop design

Abstract

The paper is focused on use of sequential patterns and frequent itemsets in electronic commerce scenarios. The aim of our research is how to use these two types of knowledge in order to support a user while browsing product catalogue with hints on products he might be interested to buy next during his visit at the seller's site. There exist applications that use other methods of recommending products of potential interests to the user, e.g. based on collaborative filtering(e.g. Amazon.com). Our approach is different in that it uses both above mentioned data mining techniques at the same time. The rationale for our approach is that the decision about these hints should be based on observed frequent itemsets., optimal purchase baskets in terms of products' cross-selling effects and observed sequential patterns underlying order in purchase behaviour. In order to maximize the efficiency of our approach, other data mining methods could be used as support for our methodology, e.g. products/customers clustering or collaborative filtering. The module under consideration should continuously, in real time, during the client's visit, determine what product to display as an incentive for a client to put in his basket next.