Doctoral Dissertations

Keywords and Phrases

Non-functional attributes; Personalized services; Service aggregation; Service recommendation; Service selection; Web services

Abstract

"In order for service users to get the best service that meets their requirements, they prefer to personalize their non-functional attributes, such as reliability and price. However, the personalization makes it challenging because service providers have to deal with conflicting non-functional attributes when selecting services for users. In addition, users may sometimes want to explicitly specify their trade-offs among non-functional attributes to make their preferences known to service providers. Typically, users' service search requests with conflicting non-functional attributes may result in a ranked list of services that partially meet their needs. When this happens, it is natural for users to submit other similar requests, with varying preferences on non-functional attributes, in an attempt to find services that fully meet their needs. This situation produces a challenge for the users to choose an optimal service based on their preferences, from the multiple ranked lists that partially satisfy their request.

Existing memory-based collaborative filtering (CF) service recommendation methods that employ this recommendation technique usually depend on non-functional attribute values obtained at service invocation to compute the similarity between users or items, and also to predict missing non-functional attributes. However, this approach is not sufficient because the non-functional attribute values of invoked services may not necessarily satisfy their personalized preferences.

The main contributions of this work are threefold. First, a novel service selection method, which is based on fuzzy logic, that considers users' personalized preferences and their trade-offs on non-functional attributes during service selection is presented. Second, a method that aggregates multiple ranked lists of services into a single aggregated ranked list, where top ranked services are selected for the user is also presented. Two algorithms were proposed: 1) Rank Aggregation for Complete Lists (RACoL), that aggregates complete ranked lists and 2) Rank Aggregation for Incomplete Lists (RAIL) to aggregate incomplete ranked lists. Finally, a CF-based service recommendation method that considers users' personalized preference on non-functional attributes if proposed. Examples using real-world services are presented to evaluate the proposed methods and experiments are carried out to validate their performance"--Abstract, page iii.

Advisor(s)

Liu, Xiaoqing Frank

Committee Member(s)

Cheng, Maggie Xiaoyan
Jiang, Wei
Chellappan, Sriram
Liou, Frank W.

Department(s)

Computer Science

Degree Name

Ph. D. in Computer Science

Publisher

Missouri University of Science and Technology

Publication Date

Fall 2015

Pagination

xi, 95 pages

Note about bibliography

Includes bibliographic references (pages 88-94).

Rights

© 2015 Kenneth Kofi Fletcher, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Subject Headings

Information storage and retrieval systemsWeb servicesIntelligent control systemsSemantic computing

Thesis Number

T 10822

Electronic OCLC #

936206654

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