The Partial Evaluation Approach to Information Personalization

Information personalization refers to the automatic adjustment of information content, structure, and presentation tailored to an individual user. By reducing information overload and customizing information access, personalization systems have emerged as an important segment of the Internet economy. This paper presents a systematic modeling methodology - PIPE (`Personalization is Partial Evaluation') - for personalization. Personalization systems are designed and implemented in PIPE by modeling an information-seeking interaction in a programmatic representation. The representation supports the description of information-seeking activities as partial information and their subsequent realization by partial evaluation, a technique for specializing programs. We describe the modeling methodology at a conceptual level and outline representational choices. We present two application case studies that use PIPE for personalizing web sites and describe how PIPE suggests a novel evaluation criterion for information system designs. Finally, we mention several fundamental implications of adopting the PIPE model for personalization and when it is (and is not) applicable.Comment: Comprehensive overview of the PIPE model for personalizatio

Statistics Notes

A collection of terms, definitions, formulas and explanations about statistics

Recommender Systems Research

We outline the history of recommender systems from their roots in information retrieval and filtering to their role in today’s Internet economy. Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. Research in recommender systems lies at the intersection of several areas of computer science, such as artificial intelligence and human-computer interaction, and has progressed to an important research area of its own. It is important to note that recommendations are not delivered within a vacuum, but rather cast within an informal community of users and social context. Ultimately all recommender systems make connections among people. This observation is under-emphasized in the recommender systems literature. Thus, we pay particular attention to the inherently social aspect of recommender systems and the connections among users that they foster. This approach represents a departure from the traditional content-based filtering versus collaborative design perspective. As we show, recommender systems connect people either directly as a result of explicit user modeling or indirectly through the discovery of relationships implicit in extant data. Thus, we characterize recommender systems by how they model users to bring people together: explicitly or implicitly. Such user modeling as well as a connection-centric viewpoint raise broadening and social issues such as evaluation, targeting, and privacy and trust which we also briefly address. Lastly, we introduce shilling, the newest issue facing recommender system researchers. A shilling attack on a recommender system involves inundating the system with data intended to coerce it to artificially recommend the perpetrator’s products more often than those of a competitor

The Design of an Emerging/Multi-Paradigm Programming Languages Course

We present the design of a new special topics course, Emerging/Multi-paradigm Languages, on the recent trend toward more dynamic, multi-paradigm languages. To foster course adoption, we discuss the design of the course, which includes language presentations/papers and culminating, inal projects/papers. The goal of this article is to inspire and facilitate course adoption

Mining Mixed-Initiative Dialogs

Human-computer dialogs are an important vehicle through which to produce a rich and compelling form of human-computer interaction. We view the specification of a human-computer dialog as a set of sequences of progressive interactions between a user and a computer system, and mine partially ordered sets, which correspond to mixing dialog initiative, embedded in these sets of sequences—a process we refer to as dialog mining—because partially ordered sets can be advantageously exploited to reduce the control complexity of a dialog implementation. Our mining losslessly compresses the specification of a dialog. We describe our mining algorithm and report the results of a simulation-oriented evaluation. Our algorithm is sound, and our results indicate that it can compress nearly all dialog specifications, and some to a high degree. This work is part of broader research on the specification and implementation of mixed-initiative dialogs

Personalization by website transformation: Theory and practice

We present an analysis of a progressive series of out-of-turn transformations on a hierarchical website to personalize a user’s interaction with the site. We formalize the transformation in graph-theoretic terms and describe a toolkit we built that enumerates all of the traversals enabled by every possible complete series of these transformations in any site and computes a variety of metrics while simulating each traversal therein to qualify the relationship between a site’s structure and the cumulative effect of support for the transformation in a site. We employed this toolkit in two websites. The results indicate that the transformation enables users to experience a vast number of paths through a site not traversable through browsing and demonstrate that it supports traversals with multiple steps, where the semblance of a hierarchy is preserved, as well as shortcuts directly to the desired information

Metalogic Notes

A collection of notes, formulas, theorems, postulates and terminology in symbolic logic, syntactic notions, semantic notions, linkages between syntax and semantics, soundness and completeness, quantified logic, first-order theories, Goedel\u27s First Incompleteness Theorem and more

Symbolic Links in the Open Directory Project

We present a study to develop an improved understanding of symbolic links in web directories. A symbolic link is a hyperlink that makes a directed connection from a web page along one path through a directory to a page along another path. While symbolic links are ubiquitous in web directories such as Yahoo!, they are under-studied, and as a result, their uses are poorly understood. A cursory analysis of symbolic links reveals multiple uses: to provide navigational shortcuts deeper into a directory, backlinks to more general categories, and multiclassification. We investigated these uses in the Open Directory Project (ODP), the largest, most comprehensive, and most widely distributed human-compiled taxonomy of links to websites, which makes extensive use of symbolic links. The results reveal that while symbolic links in ODP are used primarily for multiclassification, only few multiclassification links actually span top- and second-level categories. This indicates that most symbolic links in ODP are used to create multiclassification between topics nested more than two levels deep and suggests that there may be multiple uses of multiclassification links. We also situate symbolic links vis à vis other semantic and structural link types from hypermedia. We anticipate that the results and relationships identified and discussed in this paper will provide a foundation for (1) users for understanding the usages of symbolic links in a directory, (2) designers to employ symbolic links more effectively when building and maintaining directories and for crafting user interfaces to them, and (3) information retrieval researchers for further study of symbolic links in web directories