A Large-Itemset-Based Method for the Incremental Update of Supporting Personalized Information Filtering on the Internet

Abstract

Information filtering is an area of research that develops tools for discriminating between relevant and irrelevant information. Users first give descriptions about what they need, i.e., user profiles, to start the services. A profile index is built on these profiles. Then, the web page will be recommended to the users whose profiles belong to the filtered results. Therefore, a critical issue of the information filtering service is how to index the user profiles for an efficient matching process. Indexing user profiles can reduce the costs of storage space and the processing time for modifying the user profiles. However, when someone's interests are often changed, we must care about the way to provide the low update cost of the index structure. Therefore, in this paper, we propose a large-itemset-based method for the incremental update of the index structure for storing keywords to reduce the update cost. According to our simulation results, our method really can reduce the update cost as needed by Wu and Chen's method.