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

Survey of data mining approaches to user modeling for adaptive hypermedia

The ability of an adaptive hypermedia system to create tailored environments depends mainly on the amount and accuracy of information stored in each user model. Some of the difficulties that user modeling faces are the amount of data available to create user models, the adequacy of the data, the noise within that data, and the necessity of capturing the imprecise nature of human behavior. Data mining and machine learning techniques have the ability to handle large amounts of data and to process uncertainty. These characteristics make these techniques suitable for automatic generation of user models that simulate human decision making. This paper surveys different data mining techniques that can be used to efficiently and accurately capture user behavior. The paper also presents guidelines that show which techniques may be used more efficiently according to the task implemented by the applicatio

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

Use-cases on evolution

This report presents a set of use cases for evolution and reactivity for data in the Web and Semantic Web. This set is organized around three different case study scenarios, each of them is related to one of the three different areas of application within Rewerse. Namely, the scenarios are: “The Rewerse Information System and Portal”, closely related to the work of A3 – Personalised Information Systems; “Organizing Travels”, that may be related to the work of A1 – Events, Time, and Locations; “Updates and evolution in bioinformatics data sources” related to the work of A2 – Towards a Bioinformatics Web

Resolving the personalization-privacy dilemma: theory and implications of a privacy-preserving contract

Working papers seriesPersonalization is an integral part of e-commerce strategy today. A unique feature of personalization is that it requires users to provide a certain amount of personal information to the service provider, thus giving rise to an interesting dilemma in that consumers cannot enjoy more personalized services without sacrificing more privacy. In this paper, we propose a mechanism that allows an online personalization vendor to provide proper incentives for consumers to share information, while protecting their privacy at the same time. The proposed solution not only enables consumers and the firm to engage in an otherwise unviable market, but it also allows the firm to implement an incentive-compatible menu that serves all consumers regardless of their privacy sensitivity. Further, we demonstrate that a minimum privacy-preservation policy is an effective device for protecting consumers’ online privacy, and that it outperforms restricting vendors’ ability in collecting customer information. Our proposed mechanism is of theoretical and practical importance: By transforming the compensation schedule (privacy preservation) into a set-compliment device to the production variable, our approach offers an alternative to the reliance on external transfer, thus eradicating a major constraint confronted by traditional mechanism design. Practically, our research proposes a realistic, easily-implementable solution to the fervent calls for endowing consumers with greater control over their online privacy. Further, it offers important policy guidelines to the regulator on not only what devices can be applied in governing the information practice of online vendors, but also exactly how social-efficiency can be enhanced.preprin