| dc.description.abstract | In the mid-1990s, recommender systems have been concerned by researchers. The main task of recommender system is providing information, which can matches latent interests of customers. The recommender system is aimed to suggest and provide information of the products to customers to help them find product which they need quickly. There are mainly three kinds of methods in recommender system: Contents-based, Collaborative-filtering and Hybrid recommendation.
The collaborative filtering is the most popular and successful recommender system, it still has several limitations. First, there is no way to recommend items in sequence. Second, the success of the collaborative recommender system depends on the availability of mass users. In this paper, we focus on the first limitations and attempt to remedy this limitation of collaborative-filtering recommendation by developing the novel approach to group these sequential transactions. The key idea of our approach from the following important observation: As we know intimately, the behavior for purchasing is influenced by sequence relationship among items. For instance, such as customer may buy jelly after buying toast. It is very useful for us to understand the motivation for purchasing which is hidden behind the behavior for purchasing. It means that we need to recognize what sequential purchase behaviors is user actually follows. Then, we use that the information of customers to recommend items to customers.
Besides, we offer a new measure to compute similarity between sequences. Differing from other similarity measures, we provide a distance-sensitive similarity measure. Thus, the performance of our measure is better. | en_US |