PIPE: Web personalization by partial evaluation

Ramakrishnan, Naren

doi:10.1109/4236.895012

Cited by 27 publications

(18 citation statements)

References 57 publications

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“…PIPE is an approach to personalizing information-seeking interactions by transforming programmatic representations. PIPE models personalization as a form of partial evaluation [93], an automatic technique for specializing programs given some of their input. PIPE models can be mapped onto 5SL models allowing the 5S approach to bring the full power of transformation to bear upon diverse information resources.…”

Section: Personalization Of the 5s Approach For Generation Of Dlsmentioning

confidence: 99%

Scenario-Based Generation of Digital Library Services

Kelapure

Gonçalves

Fox

2003

Research and Advanced Technology for Digital Libraries

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(Abstract)With the enormous amount of information being created digitally or converted to digital formats and made available through Digital Libraries (DLs), there is a strong demand for building tailored DL services to attend the preferences and needs of diverse targeted communities. However, construction and adaptation of such services takes significant effort when not assisted by methodologies, tools, and environments that support the complete life cycle of DL development, including requirements gathering, conceptual modeling, rapid prototyping, and code generation/reuse. With current systems, these activities are only partially supported, generally in an uncorrelated way that may lead to inconsistencies and incompleteness. Moreover, such existing approaches are not buttressed by comprehensive and formal foundations and theories. To address these issues we describe the development, implementation, and deployment of a new generic digital library generator yielding implementations of digital library services from models of DL "societies" and "scenarios". The distinct aspects of our solution are: 1) an approach based on a formal, theoretical framework; 2) use of state-of-the-art database and software engineering techniques such as domain-specific declarative languages, scenario-synthesis, and componentized and model-driven architectures; 3) analysis centered on scenario-based design and DL societal relationships; 4) automatic transformations and mappings from scenarios to workflow designs and from these to Java implementations; and 5) special attention paid to issues of simplicity of implementation, modularity, reusability, and extensibility. We demonstrate the feasibility of the approach through a number of examples.Thanks is given to the AmericanSouth.org project funded by the Mellon Foundation.iii

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Section: Personalization Of the 5s Approach For Generation Of Dlsmentioning

confidence: 99%

Scenario-Based Generation of Digital Library Services

Kelapure

Gonçalves

Fox

2003

Research and Advanced Technology for Digital Libraries

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“…Two commonly used techniques for personalization are the content-based approach [5,[8][9][10][11] and the collaborative approach [12][13][14][15]. In the content-based approach, the system recommends each user item according to similar items the user prefers (i.e., relying on a comparison of the system's content).…”

Section: Introductionmentioning

confidence: 99%

Personalized Course Navigation Based on Grey Relational Analysis

2005

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Web personalization has quickly changed from a value-added facility to a service required in presenting large quantities of information because individual users of the Internet have various needs and preferences in seeking information. This paper presents a novel personalized recommendation system with online preference analysis in a distance learning environment called Coursebot. Users can both browse and search for course materials by using the interface of Coursebot. Moreover, the proposed system includes appropriate course materials ranked according to a user's interests. In this work, an analysis measure is proposed to combine typical grey relational analysis and implicit rating, and thus a user's interests are calculated from the content of documents and the user's browsing behavior. This algorithm's low computational complexity and ease of adding knowledge support online personalized analysis. In addition, the user profiles are dynamically revised to provide efficiently personalized information that reflects a user's interests after each page is visited.

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“…In addition, a variety of other information spaces and corresponding information-seeking activities can be modeled in PIPE. Modeling options for representing information integration, abstracting within a web page, interacting with recommender systems, modeling clickable maps, representing computed information, and capturing syntactic and semantic constraints pertaining to browsing hierarchies are described in [Ram00,RP01]. Opportunities to curtail the cost of partial evaluation for large sites are also described in [RP01].…”

Section: Modeling In Pipementioning

confidence: 99%

Personalizing Interactions with Information Systems

Perugini¹,

Ramakrishnan²

2003

Advances in Computers

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Personalization constitutes the mechanisms and technologies necessary to customize information access to the end-user. It can be defined as the automatic adjustment of information content, structure, and presentation tailored to the individual. In this chapter, we study personalization from the viewpoint of personalizing interaction. The survey covers mechanisms for information-finding on the web, advanced information retrieval systems, dialogbased applications, and mobile access paradigms. Specific emphasis is placed on studying how users interact with an information system and how the system can encourage and foster interaction. This helps bring out the role of the personalization system as a facilitator which reconciles the user's mental model with the underlying information system's organization. Three tiers of personalization systems are presented, paying careful attention to interaction considerations. These tiers show how progressive levels of sophistication in interaction can be achieved. The chapter also surveys systems support technologies and niche application domains.

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PIPE: Web personalization by partial evaluation

Cited by 27 publications

References 57 publications

Scenario-Based Generation of Digital Library Services

Scenario-Based Generation of Digital Library Services

Personalized Course Navigation Based on Grey Relational Analysis

Personalizing Interactions with Information Systems

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