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

Program Transformations for Information Personalization

Personalization constitutes the mechanisms necessary to automatically customize information content, structure, and presentation to the end user to reduce information overload. Unlike traditional approaches to personalization, the central theme of our approach is to model a website as a program and conduct website transformation for personalization by program transformation (e.g., partial evaluation, program slicing). The goal of this paper is study personalization through a program transformation lens and develop a formal model, based on program transformations, for personalized interaction with hierarchical hypermedia. The specific research issues addressed involve identifying and developing program representations and transformations suitable for classes of hierarchical hypermedia and providing supplemental interactions for improving the personalized experience. The primary form of personalization discussed is out-of-turn interaction—a technique that empowers a user navigating a hierarchical website to postpone clicking on any of the hyperlinks presented on the current page and, instead, communicate the label of a hyperlink nested deeper in the hierarchy. When the user supplies out-of-turn input, we personalize the hierarchy to reflect the user\u27s informational need. While viewing a website as a program and site transformation as program transformation is non-traditional, it offers a new way of thinking about personalized interaction, especially with hierarchical hypermedia. Our use of program transformations casts personalization in a formal setting and provides a systematic and implementation-neutral approach to designing systems. Moreover, this approach helped connect our work to human-computer dialog management and, in particular, mixed-initiative interaction. Putting personalized web interaction on a fundamentally different landscape gave birth to this new line of research. Relating concepts in the web domain (e.g., sites, interactions) to notions in the program-theoretic domain (e.g., programs, transformations) constitutes the creativity in this work

Personalization by Partial Evaluation.

The central contribution of this paper is to model personalization by the programmatic notion of partial evaluation.Partial evaluation is a technique used to automatically specialize programs, given incomplete information about their input.The methodology presented here models a collection of information resources as a program (which abstracts the underlying schema of organization and flow of information),partially evaluates the program with respect to user input,and recreates a personalized site from the specialized program.This enables a customizable methodology called PIPE that supports the automatic specialization of resources,without enumerating the interaction sequences beforehand .Issues relating to the scalability of PIPE,information integration,sessioniz-ling scenarios,and case studies are presented

Adaptive Municipal e-forms

Adaptation of electronic forms seems to be a step forward to reduce the burden for people who fill in forms. Municipalities more and more offer eforms online that can be used to request a municipal product or service. To create adaptive e-forms that satisfy the need of end-users, involvement of those users in design activities and evaluation is necessary. This paper describes the design of adaptive municipal e-forms and the way user-groups were involved in the design activities and will be involved in evaluation

Personalization via collaboration in web retrieval systems: a context based approach

World Wide Web is a source of information, and searches on the Web can be analyzed to detect patterns in Web users' search behaviors and information needs to effectively handle the users' subsequent needs. The rationale is that the information need of a user at a particular time point occurs in a particular context, and queries are derived from that need. In this paper, we discuss an extension of our personalization approach that was originally developed for a traditional bibliographic retrieval system but has been adapted and extended with a collaborative model for the Web retrieval environment. We start with a brief introduction of our personalization approach in a traditional information retrieval system. Then, based on the differences in the nature of documents, users and search tasks between traditional and Web retrieval environments, we describe our extensions of integrating collaboration in personalization in the Web retrieval environment. The architecture for the extension integrates machine learning techniques for the purpose of better modeling users' search tasks. Finally, a user-oriented evaluation of Web-based adaptive retrieval systems is presented as an important aspect of the overall strategy for personalization

Affective Music Information Retrieval

Much of the appeal of music lies in its power to convey emotions/moods and to evoke them in listeners. In consequence, the past decade witnessed a growing interest in modeling emotions from musical signals in the music information retrieval (MIR) community. In this article, we present a novel generative approach to music emotion modeling, with a specific focus on the valence-arousal (VA) dimension model of emotion. The presented generative model, called \emph{acoustic emotion Gaussians} (AEG), better accounts for the subjectivity of emotion perception by the use of probability distributions. Specifically, it learns from the emotion annotations of multiple subjects a Gaussian mixture model in the VA space with prior constraints on the corresponding acoustic features of the training music pieces. Such a computational framework is technically sound, capable of learning in an online fashion, and thus applicable to a variety of applications, including user-independent (general) and user-dependent (personalized) emotion recognition and emotion-based music retrieval. We report evaluations of the aforementioned applications of AEG on a larger-scale emotion-annotated corpora, AMG1608, to demonstrate the effectiveness of AEG and to showcase how evaluations are conducted for research on emotion-based MIR. Directions of future work are also discussed.Comment: 40 pages, 18 figures, 5 tables, author versio