A.: A conceptual clustering approach for user profiling in personal information agents

Information agents have emerged in the last decade as an alternative to assist users to cope with the increas-ing volume of information available on the Web. In order to provide personalized assistance, these agents rely on having some knowledge about users contained into user profiles, i.e. models of users preferences and interests gathered by observation of user behavior. User profiles have to summarize categories correspond-ing not only to diverse user information interests but also to different levels of abstraction in order to al-low agents to decide on the relevance of new pieces of information. In accomplishing this goal, the discovery of interest categories using document clustering offers the advantage that an a priori knowledge of user in-terests is not needed, therefore the process of acquir-ing profiles is is completely unsupervised. However, most document clustering algorithms are not applica-ble to the problem of incrementally acquiring and mod-eling interests because of either the kind of solutions they provide, which do no resemble user interests, or the way they build such solutions, which is generally no incremental. In this paper we describe and evalu-ate a document clustering algorithm, named WebDCC (Web Document Conceptual Clustering), designed to support learning of user interests by personal infor-mation agents. WebDCC algorithm carries out incre-mental, unsupervised concept learning over Web docu-ments with the goal of building and maintaining both accurate and comprehensible user profiles. Empirical evaluation of using this algorithm for user profiling and its advantages respect of other clustering algorithms are presented