Mike Perkowitz

AIJ Journal 2000 Journal Article

Towards adaptive Web sites: Conceptual framework and case study

• Mike Perkowitz
• Oren Etzioni

Today's Web sites are intricate but not intelligent; while Web navigation is dynamic and idiosyncratic, all too often Web sites are fossils cast in HTML. In response, this paper investigates adaptive Web sites: sites that automatically improve their organization and presentation by learning from visitor access patterns. Adaptive Web sites mine the data buried in Web server logs to produce more easily navigable Web sites. To demonstrate the feasibility of adaptive Web sites, the paper considers the problem of index page synthesis and sketches a solution that relies on novel clustering and conceptual clustering techniques. Our preliminary experiments show that high-quality candidate index pages can be generated automatically, and that our techniques outperform existing methods (including the Apriori algorithm, K-means clustering, hierarchical agglomerative clustering, and COBWEB) in this domain.

IJCAI Conference 1999 Conference Paper

Adaptive Web Sites: Conceptual Cluster Mining

• Mike Perkowitz
• Ortn Etzioni

The creation of a complex web site is a thorny problem in. user interface design. In IJCAI '97, we challenged the AI community to address this problem by creating adaptive web sites. In response, we investigate the problem of index page synthesis — the automatic creation of pages that facilitate a visitor's navigation of a Web site. Previous work has employed statisti­ cal methods to generate candidate index pages that are of limited value because they do not correspond to concepts or topics that are in­ tuitive to people. In this paper we formalize index page synthesis as a conceptual clustering problem and introduce a novel approach which we call conceptual cluster mining: we search for a small number of cohesive clusters that corre­ spond to concepts in a given concept descrip­ tion language L. Next, we present SGML, an algorithm schema that combines a statistical clustering algorithm with a concept learning algorithm. The clus­ tering algorithm is used to generate seed clus­ ters, and the concept learning algorithm to de­ scribe these seed clusters using expressions in L. Finally, we offer preliminary experimental evidence that instantiations of SGML outper­ form existing algorithms (e. g. , COBWEB) in this domain.

AAAI Conference 1998 Conference Paper

Adaptive Web Sites: Automatically Synthesizing Web Pages

• Mike Perkowitz

IJCAI Conference 1997 Conference Paper

Adaptive Web Sites: an AI Challenge

• Mike Perkowitz
• Oren Etzioni

The creation of a complex web site is a thorny problem in user interface design. First, different visitors have distinct goals. Second, even a single visitor may have different needs at different times. Much of the information at the site may also be dynamic or time-dependent. Third, as the site grows and evolves, its original design may no longer be appropriate. Finally, a site may be designed for a particular purpose but used in unexpected ways. Web servers record data about user interactions and accumulate this data over time. We believe that AI techniques can be used to examine user access logs in order to automatically improve the site. We challenge the AI community to create adaptive web sites: sites that automatically improve their organization and presentation based on user access data. Several unrelated research projects in plan recognition, machine learning, knowledge representation, and user modeling have begun to explore aspects of this problem. We hope that posing this challenge explicitly will bring these projects together and stimulate fundamental AI research. Success would have a broad and highly visible impact on the web and the AI community.

IJCAI Conference 1995 Conference Paper

Category Translation: Learning to understand information on the Internet

• Mike Perkowitz
• Oren Etzioni

This paper investigates the problem of automatically learning declarative models of information sources available on the Internet. We report on ILA, a domain-independent program that learns the meaning of external information by explaining it in terms of internal categories. In our experiments, ILA starts with knowledge of local faculty members, and is able to learn models of the Internet service whois and of the personnel directories available at Berkeley, Brown, Caltech, Cornell, Rice, Rutgers, and UC1, averaging fewer than 40 queries per information source. ILA's hypothesis language is compositions of first-order predicates, and its bias is compactly encoded as a determination. We analyze ILA's sample complexity both within the Valiant model, and using a probabilistic model specifically tailored to ILA.

AAAI Conference 1994 Short Paper

Database Learning for Software Agents

• Mike Perkowitz

With the amount of information available rapidly outstripping the ability of individuals to use it, we wish to explore how a software agent can learn a description of an information resource (such as a database on the internet) in order turn it into a well-understood tool at the agent’s disposal. An agent who could do this would have access to all the information it could find without having to cache the internet.